| On the approximation of functions by tanh neural networks T De Ryck, S Lanthaler, S Mishra Neural Networks, 2021 | 115 | 2021 |
| Error estimates for physics informed neural networks approximating the Navier-Stokes equations T De Ryck, AD Jagtap, S Mishra IMA Journal of Numerical Analysis, 2022 | 96 | 2022 |
| Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs T De Ryck, S Mishra Advances in Computational Mathematics 48 (6), 79, 2022 | 66 | 2022 |
| Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation T De Ryck, M De Vos, A Bertrand IEEE Transactions on Signal Processing, 2021 | 59 | 2021 |
| Generic bounds on the approximation error for physics-informed (and) operator learning T De Ryck, S Mishra Advances in Neural Information Processing Systems 35, 2022 | 51 | 2022 |
| Convolutional neural operators for robust and accurate learning of PDEs B Raonic, R Molinaro, T De Ryck, T Rohner, F Bartolucci, R Alaifari, ... Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 43* | 2023 |
| Variable-Input Deep Operator Networks M Prasthofer, T De Ryck, S Mishra arXiv preprint arXiv:2205.11404, 2022 | 23 | 2022 |
| 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 | 18* | 2024 |
| Error analysis for deep neural network approximations of parametric hyperbolic conservation laws T De Ryck, S Mishra Mathematics of Computation, 2023 | 8 | 2023 |
| On the approximation of rough functions with deep neural networks T De Ryck, S Mishra, D Ray SeMA Journal, 2019 | 7 | 2019 |
| An operator preconditioning perspective on training in physics-informed machine learning T De Ryck, F Bonnet, S Mishra, E de Bézenac arXiv preprint arXiv:2310.05801, 2023 | 5 | 2023 |
| Error estimates for physics informed neural networks approximating the Navier-Stokes equations. arXiv 2022 T De Ryck, AD Jagtap, S Mishra arXiv preprint arXiv:2203.09346, 0 | 5 | |
| On the Approximation of Rough Functions with Artificial Neural Networks T De Ryck ETH Zurich, 2020 | 1 | 2020 |
| Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning T De Ryck, S Mishra arXiv preprint arXiv:2402.10926, 2024 | | 2024 |
