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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)
; 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 ...
Machine learning discovery of optimal quadrature rules for isogeometric analysis
Teijeiro, T.; Taylor, J.M.; Hashemian, A.; Pardo, D. (2023-11-01)
; 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 ...
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)
; 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 ...
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)
; 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 ...
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)
; 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 ...
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)
; 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 ...
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)
; 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 ...
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)
; 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, ...
A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data
Abedi, M.M.; Pardo, D. (2022-12-06)
; 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 ...
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)
; 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 ...