| Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs S Mishra, R Molinaro IMA Journal of Numerical Analysis 42 (2), 981-1022, 2022 | 292 | 2022 |
| Estimates on the generalization error of physics-informed neural networks for approximating PDEs S Mishra, R Molinaro IMA Journal of Numerical Analysis 43 (1), 1-43, 2023 | 111 | 2023 |
| Physics informed neural networks for simulating radiative transfer S Mishra, R Molinaro Journal of Quantitative Spectroscopy and Radiative Transfer 270, 107705, 2021 | 99 | 2021 |
| Convolutional neural operators for robust and accurate learning of PDEs B Raonic, R Molinaro, T De Ryck, T Rohner, F Bartolucci, R Alaifari, ... Advances in Neural Information Processing Systems 36, 2024 | 43* | 2024 |
| Embedding data analytics and CFD into the digital twin concept R Molinaro, JS Singh, S Catsoulis, C Narayanan, D Lakehal Computers & Fluids 214, 104759, 2021 | 38 | 2021 |
| A multi-level procedure for enhancing accuracy of machine learning algorithms KO Lye, S Mishra, R Molinaro European Journal of Applied Mathematics 32 (3), 436-469, 2021 | 34 | 2021 |
| Physics informed neural networks (PINNs) for approximating nonlinear dispersive PDEs G Bai, U Koley, S Mishra, R Molinaro arXiv preprint arXiv:2104.05584, 2021 | 29 | 2021 |
| wPINNs: Weak physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws T De Ryck, S Mishra, R Molinaro SIAM Journal on Numerical Analysis 62 (2), 811-841, 2024 | 19* | 2024 |
| Neural inverse operators for solving PDE inverse problems R Molinaro, Y Yang, B Engquist, S Mishra arXiv preprint arXiv:2301.11167, 2023 | 19 | 2023 |
| Nonlinear reconstruction for operator learning of pdes with discontinuities S Lanthaler, R Molinaro, P Hadorn, S Mishra arXiv preprint arXiv:2210.01074, 2022 | 19 | 2022 |
| Are neural operators really neural operators? frame theory meets operator learning F Bartolucci, E de Bézenac, B Raonić, R Molinaro, S Mishra, R Alaifari arXiv preprint arXiv:2305.19913, 2023 | 12 | 2023 |
| On the paradigm of combining data analytics and CFD D Lakehal, R Molinaro AIP Conference Proceedings 2293 (1), 2020 | 2 | 2020 |
| Physics Informed Neural Networks for Thermal Analysis of Laser Powder Bed Fusion Process E Hosseini, PG Ghanbari, O Müller, R Molinaro, S Mishra Available at SSRN 4189609, 0 | 2 | |
| Applications of Deep Learning to Scientific Computing R Molinaro ETH Zurich, 2023 | 1 | 2023 |
Siddhartha MishraProfessor of Applied Mathematics, ETH Zurich, SwitzerlandEmail verificata su sam.math.ethz.ch
