Tim De Ryck

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 18

Top Topics

Top Venues

SIAM J. Numer. Anal.

IEEE Trans. Signal Process.

Publications

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) (2024)
Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
An operator preconditioning perspective on training in physics-informed machine learning. ICLR (2024)
Tim De Ryck, Siddhartha Mishra
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. CoRR (2024)
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)
Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
An operator preconditioning perspective on training in physics-informed machine learning. CoRR (2023)
Tim De Ryck, Siddhartha Mishra
Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs. Adv. Comput. Math. 48 (6) (2022)
Tim De Ryck, Siddhartha Mishra
Generic bounds on the approximation error for physics-informed (and) operator learning. CoRR (2022)
Tim De Ryck, Siddhartha Mishra
Generic bounds on the approximation error for physics-informed (and) operator learning. NeurIPS (2022)
Tim De Ryck, Siddhartha Mishra
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws. CoRR (2022)
Tim De Ryck, Siddhartha Mishra, Roberto Molinaro
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws. CoRR (2022)
Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra
Error estimates for physics informed neural networks approximating the Navier-Stokes equations. CoRR (2022)
Michael Prasthofer, Tim De Ryck, Siddhartha Mishra
Variable-Input Deep Operator Networks. CoRR (2022)
Tim De Ryck, Siddhartha Mishra
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs. CoRR (2021)
Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
On the approximation of functions by tanh neural networks. Neural Networks 143 (2021)
Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
On the approximation of functions by tanh neural networks. CoRR (2021)
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 (2021)
Tim De Ryck, Maarten De Vos, Alexander Bertrand
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation. CoRR (2020)
Tim De Ryck, Siddhartha Mishra, Deep Ray
On the approximation of rough functions with deep neural networks. CoRR (2019)