| Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems P Escapil-Inchauspé, GA Ruz Neurocomputing 561, 126826, 2023 | 28 | 2023 |
| Accelerated Calderón preconditioning for Maxwell transmission problems A Kleanthous, T Betcke, DP Hewett, P Escapil-Inchauspé, ... Journal of Computational Physics 458, 111099, 2022 | 15 | 2022 |
| Fast Calderón preconditioning for the electric field integral equation P Escapil-Inchauspé, C Jerez-Hanckes IEEE Transactions on Antennas and Propagation 67 (4), 2555-2564, 2019 | 15 | 2019 |
| Helmholtz scattering by random domains: first-order sparse boundary element approximation P Escapil-Inchauspé, C Jerez-Hanckes SIAM Journal on Scientific Computing 42 (5), A2561-A2592, 2020 | 10 | 2020 |
| Bi-parametric operator preconditioning P Escapil-Inchauspé, C Jerez-Hanckes Computers & Mathematics with Applications 102, 220-232, 2021 | 8 | 2021 |
| Local multiple traces formulation for electromagnetics: Stability and preconditioning for smooth geometries A Ayala, X Claeys, P Escapil-Inchauspé, C Jerez-Hanckes Journal of Computational and Applied Mathematics 413, 114356, 2022 | 6 | 2022 |
| Physics-informed neural networks for operator equations with stochastic data P Escapil-Inchauspé, GA Ruz arXiv preprint arXiv:2211.10344, 2022 | 2 | 2022 |
| Shape Uncertainty Quantification for Electromagnetic Wave Scattering via First-Order Sparse Boundary Element Approximation P Escapil-Inchauspé, C Jerez-Hanckes IEEE Transactions on Antennas and Propagation, 2024 | 1 | 2024 |
| Author Correction: h-Analysis and data-parallel physics-informed neural networks P Escapil‑Inchauspé, GA Ruz Scientific Reports 14, 2024 | | 2024 |
| h-Analysis and data-parallel physics-informed neural networks P Escapil-Inchauspé, GA Ruz Scientific Reports 13 (1), 17562, 2023 | | 2023 |
| High performance preconditioning and perturbation analysis applied to wave propagation problems P Escapil-Inchauspé PQDT-Global, 2021 | | 2021 |
| Shape differentiability of Helmholtz scattering problems via explicit shape calculus P Escapil-Inchauspé, C Jerez-Hanckes arXiv preprint arXiv:2011.10344, 2020 | | 2020 |
| Physics informed Neural Network for quasistatic fault slip forward and inverse problems S Cobaise, A Osses, F Ortega-Culaciati, P Escapil-Inchauspé BOOK OF, 130, 0 | | |
| PHYSICS INFORMED NEURAL NETWORK FOR QUASISTATIC FAULT SLIP FORWARD AND INVERSE PROBLEMS SC SALAS, AO ALVARADO, F ORTEGA-CULACIATI, ... | | |
| Implementation of local multiple traces formulation for electromagnetic scattering by complex objects T Betcke, P Escapil-Inchauspé, C Jerez-Hanckes, M Scroggs BOOK OF, 225, 0 | | |
| Quantifying Atomic Position Uncertainty in Molecular Electrostatics with Poisson-Boltzmann and Boundary Elements P Escapil-Inchauspé, MG Dıaz, C Jerez-Hanckes, CD Cooper BOOK OF, 229, 0 | | |
| SANTIAGO NUMERICO III P Escapil-Inchauspé, C Jerez-Hanckes | | |
| WAVE DIFFRACTION BY RANDOM SURFACES: UNCERTAINTY QUANTIFICATION VIA SPARSE TENSOR BOUNDARY ELEMENTS AND SHAPE CALCULUS P ESCAPIL-INCHAUSPÉ, C JEREZ-HANCKES | | |
