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2020 – today
- 2024
[j5]Tim De Ryck
, Siddhartha Mishra:
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. Acta Numer. 33: 633-713 (2024)- [j4]Tim De Ryck, Siddhartha Mishra, Roberto Molinaro:
wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws. SIAM J. Numer. Anal. 62(2): 811-841 (2024) - [c3]Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac:
An operator preconditioning perspective on training in physics-informed machine learning. ICLR 2024 - [i11]Tim De Ryck, Siddhartha Mishra:
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. CoRR abs/2402.10926 (2024) - 2023
- [c2]Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac:
Convolutional Neural Operators for robust and accurate learning of PDEs. NeurIPS 2023 - [i10]Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac:
An operator preconditioning perspective on training in physics-informed machine learning. CoRR abs/2310.05801 (2023) - 2022
- [j3]Tim De Ryck, Siddhartha Mishra:
Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs. Adv. Comput. Math. 48(6): 79 (2022) - [c1]Tim De Ryck, Siddhartha Mishra:
Generic bounds on the approximation error for physics-informed (and) operator learning. NeurIPS 2022 - [i9]Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra:
Error estimates for physics informed neural networks approximating the Navier-Stokes equations. CoRR abs/2203.09346 (2022) - [i8]Tim De Ryck, Siddhartha Mishra:
Generic bounds on the approximation error for physics-informed (and) operator learning. CoRR abs/2205.11393 (2022) - [i7]Michael Prasthofer, Tim De Ryck, Siddhartha Mishra:
Variable-Input Deep Operator Networks. CoRR abs/2205.11404 (2022) - [i6]Tim De Ryck, Siddhartha Mishra:
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws. CoRR abs/2207.07362 (2022) - [i5]Tim De Ryck, Siddhartha Mishra, Roberto Molinaro:
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws. CoRR abs/2207.08483 (2022) - 2021
- [j2]Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra:
On the approximation of functions by tanh neural networks. Neural Networks 143: 732-750 (2021) - [j1]Tim De Ryck, Maarten De Vos, Alexander Bertrand:
Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation. IEEE Trans. Signal Process. 69: 3513-3524 (2021) - [i4]Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra:
On the approximation of functions by tanh neural networks. CoRR abs/2104.08938 (2021) - [i3]Tim De Ryck, Siddhartha Mishra:
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs. CoRR abs/2106.14473 (2021) - 2020
- [i2]Tim De Ryck, Maarten De Vos, Alexander Bertrand:
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation. CoRR abs/2008.09524 (2020)
2010 – 2019
- 2019
- [i1]Tim De Ryck, Siddhartha Mishra, Deep Ray:
On the approximation of rough functions with deep neural networks. CoRR abs/1912.06732 (2019)
Coauthor Index
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