Tim De Ryck

According to our database, Tim De Ryck authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws.

Tim De Ryck, Siddhartha Mishra, Roberto Molinaro

SIAM J. Numer. Anal., 2024

Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning.

Tim De Ryck, Siddhartha Mishra

Acta Numer., 2024

An operator preconditioning perspective on training in physics-informed machine learning.

Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Convolutional Neural Operators for robust and accurate learning of PDEs.

Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Error analysis for deep neural network approximations of parametric hyperbolic conservation laws.

Tim De Ryck, Siddhartha Mishra

CoRR, 2022

Variable-Input Deep Operator Networks.

Michael Prasthofer, Tim De Ryck, Siddhartha Mishra

CoRR, 2022

Error estimates for physics informed neural networks approximating the Navier-Stokes equations.

Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra

CoRR, 2022

Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs.

Tim De Ryck, Siddhartha Mishra

Adv. Comput. Math., 2022

Generic bounds on the approximation error for physics-informed (and) operator learning.

Tim De Ryck, Siddhartha Mishra

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation.

Tim De Ryck, Maarten De Vos, Alexander Bertrand

IEEE Trans. Signal Process., 2021

On the approximation of functions by tanh neural networks.

Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra

Neural Networks, 2021

2019

On the approximation of rough functions with deep neural networks.

Tim De Ryck, Siddhartha Mishra, Deep Ray

CoRR, 2019