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+34 946 567 842
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+34 946 567 842
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dpardo@bcamath.org
Information of interest
David Pardo is a Research Professor at Ikerbasque, the University of the Basque Country UPV/EHU, and the Basque Center for Applied Mathematics (BCAM). He has published over 160 research articles and he has given over 260 presentations. In 2011, he was awarded as the best Spanish young researcher in Applied Mathematics by the Spanish Society of Applied Mathematics (SEMA). He leads a European Doctoral Network on self-explainable neural networks for solving partial differential equations, and several national research projects, as well as research contracts with national and international companies. He is now the PI of the research group on Applied Mathematical Modeling, Statistics, and Optimization (MATHMODE) at UPV/EHU and of the sister research group at BCAM on Mathematical Design, Modeling, and Simulations (MATHDES).
His research interests include physics-informed neural networks, computational electromagnetics, adaptive finite-element and discontinuous Petrov-Galerkin methods, multigrid solvers, deep learning algorithms, and multiphysics and inverse problems.
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Optimizing Variational Physics-Informed Neural Networks Using Least Squares
Uriarte, C.
; Bastidas, M.; Pardo, D.; Taylor, J.M.; Rojas, S. (2025-05-01)
Variational Physics-Informed Neural Networks often suffer from poor convergence when using stochastic gradient-descent-based optimizers. By introducing a least squares solver for the weights of the last layer of the neural ...
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A Least-Squares-Based Neural Network (LS-Net) for Solving Linear Parametric PDEs
Baharlouei, S.; Taylor, J.M.; Uriarte, C.; Pardo, D. (2025-03-15)
Developing efficient methods for solving parametric partial differential equations is crucial for addressing inverse problems. This work introduces a Least-Squares-based Neural Network (LS-Net) method for solving linear ...
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Deep Fourier Residual method for solving time-harmonic Maxwell’s equations
Taylor, J.M.; Pardo, D.; Muga, I.; Bastidas, M. (2025-02)
Solving PDEs with machine learning techniques has become a popular alternative to conventional methods. In this context, Neural networks (NNs) are among the most commonly used machine learning tools, and in those models, ...
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Development and Validation of a Health-aware Floating Offshore Wind Farm Simulation Platform: FOWLTY
Peña-Sanchez, Y.; Penalba, M.; Knudsen, T.; Nava, V.; Pardo, D. (2024-12)
Offshore wind energy is now an important game player towards the achievement of United Nations’ Sustainable Goals 7 (clean and affordable energy) and 13 (climate change) due to its huge potential but, given the harsh ...
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Gaussian Mixture autoencoder for uncertainty-aware damage identification in a Floating Offshore Wind Turbine
Fernandez-Navamuel, A.; Gorostidi, N.; Pardo, D.; Nava, V.; Chatzi, E. (2024-11)
This work proposes an uncertainty-aware approach to the inverse problem of damage identification in a Floating Offshore Wind Turbine (FOWT). We design an autoencoder architecture, where the latent space represents the ...
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Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework
Gonzalez-Sieiro, J.; Pardo, D.; Nava, V.; Calo, V.M.; Towara, M. (2024-09-09)
We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with ...
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Adaptive Deep Fourier Residual method via overlapping domain decomposition
Taylor, J.M.; Bastidas, M.; Calo, V. M.; Pardo, D. (2024-07-01)
The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural network (VPINN). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy ...
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Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach
Brevis, R.I.; Muga, I.; Pardo, D.; Rodríguez, O.A.; Van der Zee, K.G. (2024-06)
The efficient approximation of parametric PDEs is of tremendous importance in science and engineering. In this paper, we show how one can train Galerkin discretizations to efficiently learn quantities of interest of ...
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Ensemble Deep Learning for Enhanced Seismic Data Reconstruction
Abedi, M.M.; Pardo, D.; Alkhalifah, T. (2024-05)
Seismic data often contain gaps due to various obstacles in the investigated area and recording instrument failures. Deep-learning techniques offer promising solutions for reconstructing missing data parts by utilizing ...
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Deep Neural Network for damage detection in Infante Dom Henrique bridge using multi-sensor data
Fernandez-Navamuel, A.; Pardo, D.; Magalhães, F.; Zamora-Sánchez, D.; Omella, A. J.; Garcia-Sanchez, D. (2024-03-22)
This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements ...
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Semi-blind-trace algorithm for self-supervised attenuation of trace-wise coherent noise
Abedi, M.M.; Pardo, D.; Alkhalifah, T. (2024-03-01)
Trace-wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self-supervised deep learning to attenuate this type of noise, the conventional ...
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r-Adaptive deep learning method for solving partial di erential equations
Pardo, D.; Omella, A. J. (2024-01)
We introduce a Deep Neural Network (DNN) method for solving Partial Di erential Equations (PDEs) that simultaneously: (a) constructs an optimal radapted mesh, i.e., given an initial mesh, it provides optimal node ...
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Robust Variational Physics-Informed Neural Networks
Rojas, S.; Maczuga, P.; Muñoz-Matute, J.; Pardo, D.; Paszynski, M. (2024)
We introduce a Robust version of the Variational Physics-Informed Neural Networks method (RVPINNs). As in VPINNs, we define the quadratic loss functional in terms of a Petrov-Galerkin-type variational formulation of the ...
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Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem
Rodriguez, O.; Taylor, J.M.; Pardo, D. (2023-12)
Estimating subsurface properties from geophysical measurements is a common inverse problem. Several Bayesian methods currently aim to find the solution to a geophysical inverse problem and quantify its uncertainty. However, ...
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Fast parallel IGA-ADS solver for time-dependent Maxwell's equations
Los, M.; Wozniak, M.; Pingali, K.; Garcia-Castillo, L.E.; Alvarez-Aramberri, J.; Pardo, D.; Paszyński, M. (2023-12)
We propose a simulator for time-dependent Maxwell's equations with linear computational cost. We employ B-spline basis functions as considered in the isogeometric analysis (IGA). We focus on non-stationary Maxwell's equations ...
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Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
Gorostidi, N.; Pardo, D.; Nava, V. (2023-11-01)
Floating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to ...
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Machine learning discovery of optimal quadrature rules for isogeometric analysis
Teijeiro, T.; Taylor, J.M.; Hashemian, A.; Pardo, D. (2023-11-01)
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary ...
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Physics-guided deep-learning inversion method for the interpretation of noisy logging-while-drilling resistivity measurements
Noh, K.; Pardo, D.; Torres-Verdín, C. (2023-10)
Deep learning (DL) inversion is a promising method for real-Time interpretation of logging-while-drilling (LWD) resistivity measurements for well-navigation applications. In this context, measurement noise may significantly ...
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Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations
Fernandez-Navamuel, A.; Pardo, D.; Garcia-Sanchez, D.; Zamora-Sánchez, D.; Magalhães, F.; Omella, Ángel J. (2023-10)
This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage ...
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Neural network architecture optimization using automated machine learning for borehole resistivity measurements
Shahriari, M.; Pardo, D.; Kargaran, S.; Teijeiro, T. (2023-09)
Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. Using extremely large DNNs to approximate the operators is possible, ...
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An exponential integration generalized multiscale finite element method for parabolic problems
Contreras, L. F.; Pardo, D.; Abreu, E.; Muñoz-Matute, J.; Diaz, C.; Galvis, J. (2023-04-15)
We consider linear and semilinear parabolic problems posed in high-contrast multiscale media in two dimensions. The presence of high-contrast multiscale media adversely affects the accuracy, stability, and overall efficiency ...
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A Deep Double Ritz Method (D2RM) for solving Partial Differential Equations using Neural Networks
Uriarte, C.; Pardo, D.; Muga, I.; Muñoz-Matute, J. (2023-02-15)
Residual minimization is a widely used technique for solving Partial Differential Equations in variational form. It minimizes the dual norm of the residual, which naturally yields a saddle-point (min–max) problem over the ...
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On building physics-based AI models for the design and SHM of mooring systems
Nava, V.; Aristondo, A.; Varo, V.; Esteras, M.; Touzon, I.; Boto, F.; Mendikoa, I.; Ruiz-Minguela, P.; Gil-Lopez, S.; Gorostidi, N.; Pardo, D. (2023-01-01)
Expert systems in industrial processes are modelled using physics-based approaches, data-driven models or hybrid approaches in which however the underlying physical models generally constitute a separate block with respect ...
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Memory-Based Monte Carlo Integration for Solving Partial Differential Equations Using Neural Networks
Uriarte, C.; Taylor, J.M.; Pardo, D.; Rodríguez, O.A.; Vega (2023)
Monte Carlo integration is a widely used quadrature rule to solve Partial Differential Equations with neural networks due to its ability to guarantee overfitting-free solutions and high-dimensional scalability. However, ...
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Deep Fourier Residual method for solving time-harmonic Maxwell’s equations
Taylor, J.M.; Bastidas, M.; Pardo, D.; Muga, I. (2023)
Solving PDEs with machine learning techniques has become a popular alternative to conventional methods. In this context, Neural networks (NNs) are among the most commonly used machine learning tools, and in those models, ...
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Performance measures of nonstationary inventory models for perishable products under the EWA policy
Pardo, D.; Gorria, C.; Lezaun, M. (2022-12-16)
Accurately estimating key performance indicators in inventory models for perishable items is essential in order to assess and improve the management strategy of these systems. We analyse the production of platelet ...
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A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data
Abedi, M.M.; Pardo, D. (2022-12-06)
Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps with self-supervised deep learning, the network learns to predict different events from the recorded parts of data and then ...
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A painless multi-level automatic goal-oriented hp-adaptive coarsening strategy for elliptic and non-elliptic problems
Caro, F.V.; Darrigrand, V.; Alvarez-Aramberri, J.; Alberdi, E.; Pardo, D. (2022-11-01)
This work extends an automatic energy-norm $hp$-adaptive strategy based on performing quasi-optimal unrefinements to the case of non-elliptic problems and goal-oriented adaptivity. The proposed approach employs a multi-level ...
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Refined isogeometric analysis of quadratic eigenvalue problems
Hashemian, A.; Garcia, D.; Pardo, D.; Calo, V.M. (2022-07-16)
Certain applications that analyze damping effects require the solution of quadratic eigenvalue problems (QEPs). We use refined isogeometric analysis (rIGA) to solve quadratic eigenproblems. rIGA discretization, while ...
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Exploiting the Kronecker product structure of φ−functions in exponential integrators
Muñoz-Matute, J.; Pardo, D.; Calo, Victor M. (2022-05-15)
Exponential time integrators are well-established discretization methods for time semilinear systems of ordinary differential equations. These methods use (Formula presented.) functions, which are matrix functions related ...
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Supervised Deep Learning with Finite Element simulations for damage identification in bridges
Fernandez-Navamuel, A.; Zamora-Sánchez, Diego; Omella, Ángel J.; Pardo, D.; Garcia-Sanchez, David; Magalhães, Filipe (2022-04-15)
This work proposes a supervised Deep Learning approach for damage identification in bridge structures. We employ a hybrid methodology that incorporates Finite Element simulations to enrich the training phase of a Deep ...
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On quadrature rules for solving Partial Differential Equations using Neural Networks
Rivera, J.A; Taylor, J.M.; Omella, Ángel J.; Pardo, D. (2022-04-01)
Neural Networks have been widely used to solve Partial Differential Equations. These methods require to approximate definite integrals using quadrature rules. Here, we illustrate via 1D numerical examples the quadrature ...
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Error representation of the time-marching DPG scheme
Muñoz-Matute, J.; Demkowicz, Leszek; Pardo, D. (2022-03-01)
In this article, we introduce an error representation function to perform adaptivity in time of the recently developed time-marching Discontinuous Petrov–Galerkin (DPG) scheme. We first provide an analytical expression for ...
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Nonhyperbolic normal moveout stretch correction with deep learning automation
Abedi, M.M.; Pardo, D. (2022-02-15)
Normal-moveout (NMO) correction is a fundamental step in seismic data processing. It consists of mapping seismic data from recorded traveltimes to corresponding zero-offset times. This process produces wavelet stretching ...
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Deep learning enhanced principal component analysis for structural health monitoring
Fernandez-Navamuel, A.; Magalhães, F.; Zamora-Sánchez, D.; Omella, A. J.; Garcia-Sanchez, D.; Pardo, D. (2022-01-18)
This paper proposes a Deep Learning enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ a partially explainable autoencoder architecture to ...
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Deep learning enhanced principal component analysis for structural health monitoring
Fernandez-Navamuel, A.; Magalhães, Filipe; Zamora-Sánchez, Diego; Omella, Ángel J.; Garcia-Sanchez, David; Pardo, D. (2022-01-01)
This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to ...
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2.5-D Deep Learning Inversion of LWD and Deep-Sensing em Measurements Across Formations with Dipping Faults
Noh, Kyubo; Pardo, D.; Torres-Verdin, Carlos (2022-01-01)
Deep learning (DL) inversion of induction logging measurements is used in well geosteering for real-time imaging of the distribution of subsurface electrical conductivity. We develop a DL inversion workflow to solve 2.5-D ...
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1D Painless Multi-level Automatic Goal-Oriented h and p Adaptive Strategies Using a Pseudo-Dual Operator
Caro, F.V.; Darrigrand, V.; Alvarez-Aramberri, J.; Alberdi, E.; Pardo, D. (2022-01-01)
The main idea of our Goal-Oriented Adaptive (GOA) strategy is based on performing global and uniform h- or p-refinements (for h- and p-adaptivity, respectively) followed by a coarsening step, where some basis functions are ...
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Predictive Maintenance of Floating Offshore Wind Turbine Mooring Lines using Deep Neural Networks
Gorostidi, N.; Nava, V.; Aristondo, A.; Pardo, D. (2022)
The recent massive deployment of onshore wind farms has caused controversy to arise mainly around the issues of land occupation, noise and visual pollution and impact on wildlife. Fixed offshore turbines, albeit beneficial ...
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A Deep Fourier Residual Method for solving PDEs using Neural Networks
Taylor, J.M.; Pardo, D.; Muga, I. (2022)
When using Neural Networks as trial functions to numerically solve PDEs, a key choice to be made is the loss function to be minimised, which should ideally correspond to a norm of the error. In multiple problems, this ...
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Uncertainty Quantification on the Inversion of Geosteering Measurements using Deep Learning
Rivera, J.A; Shahriari, M; Pardo, D.; Omella, J.; Torres-Verdín, C. (2021-11-01)
We propose the use of a Deep Learning (DL) algorithm for the real-time inversion of electromagnetic measurements acquired during geosteering operations. Moreover, we show that when the DL algorithm is equipped with a ...
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Approximations for traveltime, slope, curvature, and geometrical spreading of elastic waves in layered transversely isotropic media
Abedi, M.M.; Pardo, D.; Stovas, A. (2021-10)
Each seismic body wave, including quasi compressional, shear, and converted wave modes, carries useful subsurface information. For processing, imaging, amplitude analysis, and forward modeling of each wave mode, we need ...
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Deep learning driven self-adaptive hp finite element method
Paszyński, M.; Grzeszczuk, R.; Pardo, D.; Demkowicz, L. (2021-06)
The fi nite element method (FEM) is a popular tool for solving engineering problems governed by Partial Di fferential Equations (PDEs). The accuracy of the numerical solution depends on the quality of the computational ...
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Massive Database Generation for 2.5D Borehole Electromagnetic Measurements using Refined Isogeometric Analysis
Hashemian, A.; Garcia, D.; Rivera, J.A.; Pardo, D. (2021-06)
Borehole resistivity measurements are routinely inverted in real-time during geosteering operations. The inversion process can be efficiently performed with the help of advanced artificial intelligence algorithms such as ...
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Large-offset P-wave traveltime in layered transversely isotropic media
Abedi, M.M.; Pardo, D. (2021-05-01)
Large-offset seismic data processing, imaging, and velocity estimation require an accurate traveltime approximation over a wide range of offsets. In layered transversely isotropic media with a vertical symmetry axis, the ...
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Modeling extra-deep electromagnetic logs using a deep neural network
Alyaev, S.; Shahriari, M.; Pardo, D.; Omella, A. J.; Larsen, D.; Jahani, N.; Suter, E. (2021-05)
Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set ...
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Refined isogeometric analysis for generalized Hermitian eigenproblems
Hashemian, A.; Pardo, D.; Calo, V.M. (2021-04)
We use refined isogeometric analysis (rIGA) to solve generalized Hermitian eigenproblems (Ku = λMu). rIGA conserves the desirable properties of maximum-continuity isogeometric analysis (IGA) while it reduces the solution ...
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A Simulation Method for the Computation of the E
Omella, A. J.; Alvarez-Aramberri, J.; Strugaru, M.; Darrigrand, V.; Pardo, D.; Gonzalez, H.; Santos, C. (2021-03)
We propose a set of numerical methods for the computation of the frequency-dependent eff ective primary wave velocity of heterogeneous rocks. We assume the rocks' internal microstructure is given by micro-computed tomography ...
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Goal-oriented adaptivity for a conforming residual minimization method in a dual discontinuous Galerkin norm
Rojas, S.; Pardo, D.; Behnoudfare, P.; Calo, V. M. (2021-03)
We propose a goal-oriented mesh-adaptive algorithm for a finite element method stabilized via residual minimization on dual discontinuous-Galerkin norms. By solving a saddle-point problem, this residual minimization delivers ...
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Vibration-Based SHM Strategy for a Real Time Alert System with Damage Location and Quantification
Fernandez - Navamuel, A.; Zamora - Sánchez, D.; Varona - Poncela, T.; Jiménez - Fernández, C.; Díez - Hernández, J.; García Sánchez, D.; Pardo, D. (2021-01-11)
We present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the ...
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Vibration-Based SHM Strategy for a Real Time Alert System with Damage Location and Quantification
Fernández-Navamuel, A.; Zamora-Sánchez, D.; Varona-Poncela, T.; Jiménez-Fernández, C.; Díez-Hernández, J.; García-Sánchez, D.; Pardo, D. (2021-01)
We present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the ...
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A Finite Element based Deep Learning solver for parametric PDEs
Uriarte, C.; Pardo, D.; Omella, A. J. (2021)
We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations(PDEs). The connections between neurons in the architecture mimic the ...
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A MULTIDIRECTIONAL DEEP NEURAL NETWORK FOR SELF-SUPERVISED RECONSTRUCTION OF SEISMIC DATA
Abedi, M.M.; Pardo, D. (2021)
Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps with self-supervised deep learning, the network learns to predict different events from the recorded parts of data and then ...
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Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements
Shahriari, M.; Pardo, D.; Rivera, J.A.; Torres-Verdín, C.; Picon, A.; Ossandón, S.; Calo, V.M.; Del Ser, J. (2020-11)
Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of ...
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A DPG-based time-marching scheme for linear hyperbolic problems
Muñoz-Matute, J.; Pardo, D.; Demkowicz, L. (2020-11)
The Discontinuous Petrov-Galerkin (DPG) method is a widely employed discretization method for Partial Di fferential Equations (PDEs). In a recent work, we applied the DPG method with optimal test functions for the time ...
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Equivalence between the DPG method and the Exponential Integrators for linear parabolic problems
Muñoz-Matute, J.; Pardo, D.; Demkowicz, L. (2020-11)
The Discontinuous Petrov-Galerkin (DPG) method and the exponential integrators are two well established numerical methods for solving Partial Di fferential Equations (PDEs) and sti ff systems of Ordinary Di fferential ...
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Bearing assessment tool for longitudinal bridge performance
Garcia-Sanchez, D.; Fernandez-Navamuel, A.; Zamora, D.; Alvear, D.; Pardo, D. (2020-09)
This work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection ...
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Sensitivity and Uncertainty Analysis by Discontinuous Galerkin of Lock-in Thermography for Crack Characterization
Omella, A. J.; Celorrio, R.; Pardo, D. (2020-09)
This work focuses on the characterization of narrow vertical cracks of nite size using optically excited lock-in thermography (OLT). To characterize these cracks, we need to solve an ill-posed inverse problem. As a previous ...
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Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements
Shahriari, M.; Rivera, J.A.; Pardo, D.; Torres-Verdín, C.; Picon, A.; Del Ser, J.; Ossandón, S.; Calo, V.M. (2020-05)
Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of ...
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Design of Loss Functions for Solving Inverse Problems using Deep Learning
Rivera, J.A.; Pardo, D.; Alberdi, E. (2020-05)
Solving inverse problems is a crucial task in several applications that strongly a ffect our daily lives, including multiple engineering fields, military operations, and/or energy production. There exist different methods ...
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A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements
Shahriari, M.; Pardo, D.; Picon, A.; Galdran, A.; Del Ser, J.; Torres-Verdin, C. (2020-04)
Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical ...
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A Deep Neural Network as Surrogate Model for Forward Simulation of Borehole Resistivity Measurements
Shahriari, M.; Pardo, D.; Moser, B. (2020-01)
Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, ...
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A Painless Automatic hp-Adaptive Strategy for Elliptic Problems
Darrigrand, V.; Pardo, D.; Chaumont-Frelet, T.; Gómez-Revuelto, I.; Garcia-Castillo, E. (2020-01)
In this work, we introduce a novel hp-adaptive strategy. The main goal is to minimize the complexity and implementational efforts hence increasing the robustness of the algorithm while keeping close to optimal numerical ...
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Borehole Resistivity Simulations of Oil-Water Transition Zones with a 1.5D Numerical Solver
Shahriari, M.; Pardo, D. (2020)
When simulating borehole resistivity measurements in a reservoir, it is common to consider an oilwater contact (OWC) planar interface. However, this consideration can lead to an unrealistic model since in the presence of ...
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Variational Formulations for Explicit Runge-Kutta Methods
Muñoz-Matute, J.; Pardo, D.; Calo, V.M.; Alberdi, E. (2019-08)
Variational space-time formulations for partial di fferential equations have been of great interest in the last decades, among other things, because they allow to develop mesh-adaptive algorithms. Since it is known ...
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Explicit-in-Time Goal-Oriented Adaptivity
Muñoz-Matute, J.; Calo, V.M.; Pardo, D.; Alberdi, E.; Van der Zee, K.G. (2019-04-15)
Goal-oriented adaptivity is a powerful tool to accurately approximate physically relevant solution features for partial differential equations. In time dependent problems, we seek to represent the error in the quantity of ...
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Forward-in-Time Goal-Oriented Adaptivity
Muñoz-Matute, J.; Pardo, D.; Calo, V.M.; Alberdi, E. (2019-03)
In goal-oriented adaptive algorithms for partial differential equations, we adapt the finite element mesh in order to reduce the error of the solution in some quantity of interest. In time-dependent problems, this adaptive ...
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Refined Isogeometric Analysis for fluid mechanics and electromagnetism
Garcia, D.; Pardo, D.; Calo, V.M. (2019-03)
Starting from a highly continuous isogeometric analysis discretization, we introduce hyperplanes that partition the domain into subdomains and reduce the continuity of the discretization spaces at these hyperplanes. As the ...
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Adjoint-based formulation for computing derivatives with respect to bed boundary positions in resistivity geophysics
Chaumont-Frelet, T.; Shahriari, M.; Pardo, D. (2019-02)
In inverse geophysical resistivity problems, it is common to optimize for specific resistivity values and bed boundary positions, as needed, for example, in geosteering applications. When using gradient-based inversion ...
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Parallel refined Isogeometric Analysis in 3D
Siwik, L.; Wozniak, M.; Trujillo, V.; Pardo, D.; Calo, V.M.; Paszynski, M. (2018-11)
We study three-dimensional isogeometric analysis (IGA) and the solution of the resulting system of linear equations via a direct solver. IGA uses highly continuous $C^{p-1}$ basis functions, which provide multiple benefits ...
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Finite element approximation of electromagnetic fields using nonfitting meshes for Geophysics
Chaumont-Frelet, T.; Nicaise, S.; Pardo, D. (2018-07)
We analyze the use of nonfitting meshes for simulating the propagation of electromagnetic waves inside the earth with applications to borehole logging. We avoid the use of parameter homogenization and employ standard edge ...
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Asymptotic Models for the Electric Potential across a Highly Conductive Casing
Erdozain, A.; Péron, V.; Pardo, D. (2018-07)
We analyze a configuration that involves a steel-cased borehole, where the casing that covers the borehole is considered as a highly conductive thin layer. We develop an asymptotic method for deriving reduced problems ...
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Refined Isogeometric Analysis for a Preconditioned Conjugate Gradient Solver
Garcia, D.; Pardo, D.; Dalcin, L.; Calo, V.M. (2018-06-15)
Starting from a highly continuous Isogeometric Analysis (IGA) discretization, refined Isogeometric Analysis (rIGA) introduces $C^0$ hyperplanes that act as separators for the direct LU factorization solver. As a result, ...
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A Numerical 1.5D Method for the Rapid Simulation of Geophysical Resistivity Measurements
Shahriari, M.; Rojas, S.; Pardo, D.; Rodríguez-Rozas, A.; Bakr, S.A.; Calo, V.M.; Muga, I. (2018-06-14)
In some geological formations, borehole resistivity measurements can be simulated using a sequence of 1D models. By considering a 1D layered media, we can reduce the dimensionality of the problem from 3D to 1.5D via a ...
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Fast 2.5D Finite Element Simulations of Borehole Resistivity Measurements
Rodriguez-Rozas, A.; Pardo, D.; Torres-Verdin, C. (2018-05-29)
We develop a rapid 2.5-dimensional (2.5D) finite element method for simulation of borehole resistivity measurements in transversely isotropic (TI) media. The method combines arbitrary high-order $H^1$ - and $H$ (curl)-conforming ...
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Finite Element Simulations of Logging-While-Drilling and Extra-Deep Azimuthal Resistivity Measurements using Non-Fitting Grids
Chaumont-Frelet, T.; Pardo, D.; Rodriguez-Rozas, A. (2018-04-27)
We propose a discretization technique using non-fitting grids to simulate magnetic field-based resistivity logging measurements. Non-fitting grids are convenient because they are simpler to generate and handle than fitting ...
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Source time reversal (STR) method for linear elasticity
Brevis, R.I.; Rodríguez-Rozas, A.; Ortega, J.H.; Pardo, D. (2018)
We study the problem of source reconstruction for a linear elasticity problem applied to seismicity induced by mining. We assume the source is written as a variable separable function $\mathbf{f(x)}\>g(t)$ . We first present ...
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Time-Domain Goal-Oriented Adaptivity Using Pseudo-Dual Error Representations
Muñoz-Matute, J.; Alberdi, E.; Pardo, D.; Calo, V.M. (2017-12)
Goal-oriented adaptive algorithms produce optimal grids to solve challenging engineering problems. Recently, a novel error representation using (unconventional) pseudo-dual problems for goal-oriented adaptivity in the ...
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Goal-oriented adaptivity using unconventional error representations for the multi-dimensional Helmholtz equation
Darrigrand, V.; Rodríguez-Rozas, A.; Muga, I.; Pardo, D.; Romkes, A.; Prudhomme, S. (2017-06-27)
In goal‐oriented adaptivity, the error in the quantity of interest is represented using the error functions of the direct and adjoint problems. This error representation is subsequently bounded above by element‐wise error ...
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1.5D BASED INVERSION OF LOGGING-WHILE-DRILLING RESISTIVITY MEASUREMENTS IN 3D FORMATIONS
Pardo, D.; Torres-Verdín, C. (2017-06)
This manuscript describes an extension of a computer method developed for the fast inversion of logging-while-drilling (LWD) resistivity measurements Pardo and Torres-Verdín (2015); Bakr et al. (2016). The method enables ...
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Fast Simulation of 2.5D LWD Resistivity Tools
Rodríguez-Rozas, A.; Pardo, D.; Torres-Verdín, C. (2017-06)
As a first step towards the fast inversion of geophysical data, in this work we focus on the rapid simulations of 2.5D logging-while-drilling (LWD) borehole resistivity measurements. Given a commercial logging instrument ...
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Optimally refined isogeometric analysis
Garcia, D.; Barton, M.; Pardo, D. (2017-06)
Performance of direct solvers strongly depends upon the employed discretization method. In particular, it is possible to improve the performance of solving Isogeometric Analysis (IGA) discretizations by introducing multiple ...
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Fusion-based variational image dehazing
Galdran, A.; Vazquez-Corral, J.; Pardo, D.; Bertalmio, M. (2017-02-01)
We propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) ...
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A source time reversal method for seismicity induced by mining
Brevis, R.I.; Ortega, J.H.; Pardo, D. (2017-01-01)
In this work, we present a modified Time-Reversal Mirror (TRM) Method, called Source Time Reversal (STR), to find the spatial distribution of a seismic source induced by mining activity. This methodology is based on a known ...
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A multi-domain decomposition-based Fourier finite element method for the simulation of 3D marine CSEM measurements
Bakr, S.A.; Pardo, D. (2017-01-01)
We introduce a multi-domain decomposition Fourier finite element (MDDFFE) method for the simulation of three-dimensional (3D) marine controlled source electromagnetic measurement (CSEM). The method combines a 2D finite ...
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Goal-Oriented p-Adaptivity using Unconventional Error Representations for a 1D Steady State Convection-Diffusion Problem
Pardo, D.; Darrigrand, V.; Rodríguez-Rozas, A.; Muga, I. (2017)
This work proposes the use of an alternative error representation for Goal-Oriented Adaptivity (GOA) in context of steady state convection dominated diffusion problems. It introduces an arbitrary operator for the computation ...
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ICCS 2017 Workshop on Agent-Based Simulations, Adaptive Algorithms and Solvers
Byrski, A.; Paszynski, M.; Schaefer, R.; Calo, V.M.; Pardo, D. (2017)
This workshop seeks to integrate results from different domains of computer science, computational science, and mathematics. We welcome simulation papers, either hard simulations using finite element or finite difference ...
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A Quadrature-Free Method for Simulation and Inversion of 1.5D Direct Current (DC) Borehole Measurements
Rojas, S.; Muga, I.; Pardo, D. (2016-12)
Resistivity inverse problems are routinely solved in order to characterize hydrocarbon bearing formations. They often require a large number of forward problems simulations. When considering a one dimensional (1D) planarly ...
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Fast inversion of logging-while-drilling resistivity measurements acquired in multiple wells
Bakr, S.A.; Pardo, D.; Torres-Verdin, C. (2016-10)
This paper introduces a new method for the fast inversion of borehole resistivity measurements acquired in multiple wells using logging-while-drilling (LWD) instruments. There are two key novel contributions. First, we ...
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The value of continuity: Refined isogeometric analysis and fast direct solvers
Garcia, D.; Pardo, D.; Dalcin, L.; Paszynski, M.; Collier, N.; Calo, V.M. (2016-09-23)
We propose the use of highly continuous finite element spaces interconnected with low continuity hyperplanes to maximize the performance of direct solvers. Starting from a highly continuous Isogeometric Analysis (IGA) ...
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A multi-objective memetic inverse solver reinforced by local optimization methods
Gajda-Zagórska, E.; Schaefer, R.; Smolka, M.; Pardo, D.; Alvarez-Aramberri, J. (2016-09-01)
We propose a new memetic strategy that can solve the multi-physics, complex inverse problems, formulated as the multi-objective optimization ones, in which objectives are misfits between the measured and simulated states ...
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Dimensionally adaptive hp-finite element simulation and inversion of 2D magnetotelluric measurements
Alvarez-Aramberri, J.; Pardo, D. (2016-09-01)
Magnetotelluric (MT) problems often contain different subdomains where the conductivity of the media depends upon one, two, or three spatial variables. Traditionally, when a MT problem incorporates a three-dimensional (3D) ...
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An Agent-Oriented Hierarchic Strategy for Solving Inverse Problems
Smolka, M.; Schaefer, R.; Paszynski, M.; Pardo, D.; Alvarez-Aramberri, J. (2015-12-31)
The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global ...
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High-accuracy adaptive modeling of the energy distribution of a meniscus-shaped cell culture in a Petri dish
Gomez-Revuelto, I.; Garcia-Castillo, L.E.; Pardo, D. (2015-12-31)
Cylindrical Petri dishes embedded in a rectangular waveguide and exposed to a polarized electromagnetic wave are often used to grow cell cultures. To guarantee the success of these cultures, it is necessary to enforce that ...
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Enhanced variational image dehazing
Galdran, A.; Vazquez-Corral, J.; Pardo, D.; Bertalmio, M. (2015-12-31)
Images obtained under adverse weather conditions, such as haze or fog, typically exhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling the image structure under the haze ...
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Goal-oriented adaptivity using unconventional error representations for the 1D Helmholtz equation
Darrigrand, V.; Pardo, D.; Muga, I. (2015-12-31)
In this work, the error of a given output functional is represented using bilinear forms that are different from those given by the adjoint problem. These representations can be employed to design novel h, p, and hp ...
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Fourier finite element modeling of light emission in waveguides: 2.5-dimensional FEM approach
Ou, Y.; Pardo, D.; Chen, Y. (2015-12-31)
We present a Fourier finite element modeling of light emission of dipolar emitters coupled to infinitely long waveguides. Due to the translational symmetry, the three-dimensional (3D) coupled waveguide-emitter system can ...
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Multi-objective hierarchic memetic solver for inverse parametric problems
Gajda-Zagórska, E.; Smolka, M.; Schaefer, R.; Pardo, D.; Alvarez-Aramberri, J. (2015-12-31)
We propose a multi-objective approach for solving challenging inverse parametric problems. The objectives are misfits for several physical descriptions of a phenomenon under consideration, whereas their domain is a common ...
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Quantities of interest for surface based resistivity geophysical measurements
Alvarez-Aramberri, J.; Bakr, S.A.; Pardo, D.; Barucq, H. (2015-12-31)
The objective of traditional goal-oriented strategies is to construct an optimal mesh that minimizes the problem size needed to achieve a user prescribed tolerance error for a given quantity of interest (QoI). Typical ...
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Automatic Red-Channel underwater image restoration
Galdran, A.; Pardo, D.; Picón, A.; Alvarez-Gila, A. (2015-12-31)
Underwater images typically exhibit color distortion and low contrast as a result of the exponential decay that light suffers as it travels. Moreover, colors associated to different wavelengths have different attenuation ...
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Direct solvers performance on h-adapted grids
Paszynski, M.; Pardo, D.; Calo, V.M. (2015-12-31)
We analyse the performance of direct solvers when applied to a system of linear equations arising from an $h$-adapted, $C^0$ finite element space. Theoretical estimates are derived for typical $h$-refinement patterns arising ...
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Semi-analytical response of acoustic logging measurements in frequency domain
Muga, I.; Pardo, D.; Matuszyk, P.J.; Torres-Verdin, C. (2015-12-31)
This work proposes a semi-analytical method for simulation of the acoustic response of multipole eccentered sources in a fluid-filled borehole. Assuming a geometry that is invariant with respect to the azimuthal and vertical ...
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Computational cost of isogeometric multi-frontal solvers on parallel distributed memory machines
Wozniak, M.; Paszynski, M.; Pardo, D.; Dalcin, L.; Calo, V.M. (2015-12-31)
This paper derives theoretical estimates of the computational cost for isogeometric multi-frontal direct solver executed on parallel distributed memory machines. We show theoretically that for the $C^{p-1}$ global continuity ...
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A Secondary Field Based hp-Finite Element Method for the Simulation of Magnetotelluric Measurements
Alvarez-Aramberri, J.; Pardo, D.; Barucq, E. (2015-12-31)
In some geophysical problems, it is sometimes possible to divide the subsurface resistivity distribution as a one dimensional (1D) contribution plus some two dimensional (2D) inhomogeneities. Assuming this scenario, we ...
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FAST AND AUTOMATIC INVERSION OF LWD RESISTIVITY MEASUREMENTS FOR PETROPHYSICAL INTERPRETATION
Pardo, D.; Torres-Verdín, C.; Ali Bakr, S. (2015-07)
This paper describes an extension of a recently developed fast inversion method (Pardo and Torres-VerdÍn (2015)) for estimating a layer-by-layer electric resistivity distribution from logging-whiledrilling (LWD) electromagnetic ...
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A hybrid method for inversion of 3D DC resistivity logging measurements
Gajda-Zagórska, E.; Schaefer, R.; Smolka, M.; Paszynski, M.; Pardo, D. (2014-12-31)
This paper focuses on the application of hp hierarchic genetic strategy (hp-HGS) for solution of a challenging problem, the inversion of 3D direct current (DC) resistivity logging measurements. The problem under consideration ...
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Fast simulation of through-casing resistivity measurements using semi-analytical asymptotic models. Part 1: Accuracy study
Erdozain, A.; Peron, V.; Pardo, D. (2014-12-31)
When trying to obtain a better characterization of the Earth's subsurface, it is common to use borehole through-casing resistivity measurements. It is also common for the wells to be surrounded by a metal casing to protect ...
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Unified modeling language description of the object-oriented multi-scale adaptive finite element method for step-and-flash imprint lithography simulations
Paszynski, M.; Gurgul, P.; Sieniek, M.; Pardo, D. (2014-12-31)
In the first part of the paper we present the multi-scale simulation of the Step-and-Flash Imprint Lithography (SFIL), a modern patterning process. The simulation utilizes the hp adaptive Finite Element Method (hp-FEM) ...
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Computational cost estimates for parallel shared memory isogeometric multi-frontal solvers
Wozniak, M.; Kuznik, K.; Paszynski, M.; Calo, V.M.; Pardo, D. (2014-12-31)
In this paper we present computational cost estimates for parallel shared memory isogeometric multi-frontal solvers. The estimates show that the ideal isogeometric shared memory parallel direct solver scales as $\mathcal{O}( ...
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Fast 1D Inversion of Logging-While-Drilling Resistivity Measurements for Improved Estimation of Formation Resistivity in High-Angle and Horizontal Wells
Pardo, D.; Torres-Verdín, C. (2014-12)
We have developed an efficient inversion method to estimate layer-by-layer electric resistivity from loggingwhile-drilling electromagnetic induction measurements. The method assumes a 1D model based on planarly layered ...
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3D hp-Adaptive Finite Element Simulations of Bend, Step, and Magic-T Electromagnetic Waveguide Structures
Gomez-Revuelto, I.; Garcia-Castillo, L.E.; Llorente-Romano, S.; Pardo, D. (2014-03)
Metallic rectangular waveguides are often the preferred choice on telecommunication systems and medical equipment working on the upper microwave and millimeter wave frequency bands due to the simplicity of its geometry, ...
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Sensitivity Analysis for the Appraisal of Hydrofractures in Horizontal Wells with Borehole Resistivity Measurements
Pardo, D.; Torres-Verdín, C. (2013-06)
This paper numerically evaluates the possibility of using borehole electromagnetic (EM) measurements to diagnose and quantify hydraulic fractures that have been arti ficially generated in a horizontal well. Hydrofractures ...
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Influence of borehole-eccentred tools on wireline and logging-while-drilling sonic logging measurements
Pardo, D.; Matuszyky, P.; Torres-Verdín, C.; Mora, A.; Muga, I. (2013-06)
We describe a numerical study to quantify the influence of tool-eccentricity on wireline and logging-while-drilling (LWD) sonic logging measurements. Simulations are performed with a hp-adaptive Fourier Finite-Element ...
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Simulation of marine controlled source electromagnetic measurements using a parallel fourier hp-finite element method
Pardo, D.; Nam, M.J.; Torres-Verdin, C.; Hoversten, M.G.; Garay Iñ. (2011-12-31)
We introduce a new numerical method to simulate geophysical marine controlled source electromagnetic (CSEM) measurements for the case of 2D structures and finite 3D sources of electromagnetic (EM) excitation. The method ...
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Modeling of bone conduction of sound in the human head using hp-finite elements: Code design and verification
Demkowicz, L.; Gatto, P.; Kurtz, J.; Paszynski, M.; Rachowicz, W.; Bleszyński, E.; Bleszyński, M.; Hamilton, M.; Champlin, C.; Pardo, D. (2011-12-31)
We focus on the development of a reliable numerical model for investigating the bone-conduction of sound in the human head. The main challenge of the problem is the lack of fundamental knowledge regarding the transmission ...
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Compensation effect analysis in DIE method for through-casing measuring formation resistivity
Qing, C.; Pardo, D.; Hong-bin, L.; Fu-rong, W. (2011-08)
The measuring technique based on Double-Injection-Electrodes (DIE) and its compensation arithmetic method have been proven to be very useful for eliminating the errors caused by electrode-scale mechanical tolerances in ...
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New post processing method for interpretation of through casing resistivity (TCR) measurements
Qing, C.; Pardo, D.; Hong-bin, L. (2011-03)
We propose a new iterative method for post processing through casing resistivity (TCR) measurements. This method can be seen as a correction or extension of Kaufman´s theory to more complex scenarios, in which the casing ...
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Arithmetic method of double-injection-electrode model for resistivity measurement through metal casing
Chen, Q.; Pardo, D.; Li, H.-B.; Wang, F.-R.; Ye, Q.-Z. (2010-12-31)
Through-casing resistivity (TCR) measurement instruments such as Cased Hole Formation Resistivity are extensively used for the dynamic monitoring of oil reservoirs during the production phase in oil wells to evaluate the ...
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Simulation of DC dual-laterolog measurements in complex formations: A Fourier-series approach with nonorthogonal coordinates and self-adapting finite elements
Nam, M.J.; Pardo, D.; Torres-Verdin, C. (2009-12-31)
Dual laterolog (DLL) makes use of a galvanic conduction principle to focus electrical currents into rock formations, thereby minimizing shoulder and borehole effects in the measurement of formation resistivity. The tool ...
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Performance of a multi-frontal parallel direct solver for hp-finite element method
Paszynski, M.; Pardo, D.; Torres-Verdin, C. (2009-12-31)
In this paper we present the performance of our parallel multi-frontal direct solver when applied to solve linear systems of equations resulting from discretizations of a hp Finite Element Method (hp-FEM). The hp-FEM ...
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Sensitivity study of borehole-to-surface and crosswell electromagnetic measurements acquired with energized steel casing to water displacement in hydrocarbon-bearing layers
Pardo, D.; Torres-Verdin, C.; Zhang, Z. (2008-12-31)
We study the theoretical response of electromagnetically energized steel casing in the presence of subsurface variations of electrical resistivity. Casing is energized with a finite-size solenoid antenna located along the ...
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Energy-norm-based and goal-oriented automatic hp adaptivity for electromagnetics: Application to waveguide Discontinuities
Garcia-Castillo, L.E.; Pardo, D.; Demkowicz, L. (2008-12-31)
The finite-element method (FEM) enables the use of adapted meshes. The simultaneous combination of h (local variations in element size) and p (local variations in the polynomial order of approximation) refinements, i.e., ...
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2024-2028 |
250,000 Euros: Spanish Ministry: PID2023-146678OB-I00: ULTRAPINNs (PI: D. Pardo, V. Nava) |
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2024-2028 |
30,000 Euros: Collaborative UPV/EHU Projects (PI: I. Barrio; Co-PI: D. Pardo). |
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2024-2025 |
2,354,400 Euros: Horizon Europe MSCA Doctoral Networks: IN-DEEP (PI: D. Pardo). |
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2024-2025 |
2,000,000 Euros: Spanish Ministry: UNICO I+D 5G-6G 2022: (TSI-064100-2022-22) (PI: E. Jacob) |
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2024-2026 |
181,152 Euros: Horizon Europe MSCA Postdoctoral Fellowship Matteo Croci: GEOLEARN (PI: D. Pardo) |
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2023-2024 |
78,000 Euros: IKUR HPC-IA MATHinDEEP (PI: D. Pardo). |
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2023-2024 |
40,000 Euros: IKUR HPC-IA DEEPFARMS (PI: V. Nava, D. Pardo). |
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2023-2024 |
120,000 Euros: IKUR HPC-IA TrafoSPINN (PI: J. Aizpurua). |
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2022-2026 |
4,000,000 Euros: BCAM “Severo Ochoa” (PI: L. Vega). D. Pardo is one of the 11 participants. |
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2022-2024 |
64,677 Euros: Contract with GKN Automotive Zumaia (PI: D. Pardo). |
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2022-2025 |
486,150 Euros: Excellent (A) Group MATHMODE -Basque Government (IT1456-22)- (PI: D. Pardo, I. Arostegui) |
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2022-2024 |
169,280 Euros: Transicion Ecologica y Digital: TED2021-132783B-I00, MATHEOLO (PI: D. Pardo, V. Nava) |
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2022-2023 |
12,000 Euros: Misiones Euskampus 2.0 (PI: T. Teijeiro, J. Alvarez-Aramberri). |
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2021-2024 |
650,000 Euros: Artificial Intelligence for Sustainable Energy Transition (IA4TES), Misiones CDTI (PI at BCAM: V. Nava and S. Mazuelas). |
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2021-2022 |
3,500 Euros: Misiones Euskampus 1.0 (PI: D. Pardo). |
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2022-2024 |
90,000 Euros: H2020 Marie Curie Cofund European Energy for Future (E4F) Ref. 101034297 (PI at UPV/EHU: D. Pardo). |
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2021-2023 |
80,500 Euros: Proof of Concept PDC2021-121093-I00, SUBEM (PI: D. Pardo). |
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2021-2024 |
238,000 Euros: H2020 Marie Curie Fellowship for J. Muñoz-Matute (GEOPDG) (PI: D. Pardo; Co-PI: L. Demkowicz). |
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2021-2022 |
82,000 Euros: IKUR HPC-IA on Deep Learning for PDEs (PI at BCAM: D. Pardo). |
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2021-2022 |
46,488 Euros: Elkartek Project ExpertIA (PI at UPV/EHU: D. Pardo). |
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2021-2022 |
48,672 Euros: Elkartek Project SIGZE (PI at UPV/EHU: D. Pardo). |
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2021-2022 |
33,120 Euros: Collaborative UPV/EHU Projects (PI: I. Arostegui; Co-PI: D. Pardo). |
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2021-2022 |
90,730 Euros: Math-in Technological Platform (PI: P. Quintela; Treasurer: D. Pardo). |
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2020-2023 |
2,200 Euros: MATHDATA: an AUIP Latin-American Network (PI: D. Pardo). |
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2020-2021 |
44,995 Euros: Contract with GKN Automotive Zumaia (PI: D. Pardo). |
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2020-2021 |
86,710 Euros: Elkartek Project 3KIA (PI at UPV/EHU: I. Barrio). |
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2020-2021 |
20,000 Euros: VIVIR, Fundacion Iberdrola (PI: V. Nava, D. Pardo). |
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2020-2023 |
136,004 Euros: PID2019-108111RB-I00, DEEPINVERSE (PI: D. Pardo). |
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2019-2021 |
180,000 Euros: PIXIL POCTEFA PROJECT - H2020 Programme- (PI at BCAM: D. Pardo). |
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2019-2020 |
60,000 Euros: BCAM Project on Artificial Intelligence for Energy (PI: D. Pardo). |
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2019 |
25,000 Euros: Contract with The University of Texas at Austin (PI: D. Pardo). |
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2019-2021 |
333,856 Euros: Excellent (A+) Group MATHMODE -Basque Government- (PI: D. Pardo). |
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2019-2020 |
49,884 Euros: Elkartek Project MATHEO (PI at UPV/EHU: D. Pardo). |
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2019 |
31,478 Euros: Elkartek Project ArgIA (PI at UPV/EHU: D. Pardo). |
See CV.
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