Tim De Ryck - RankMe

20 papers A* 3Journal 17

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs. CoRR Tim De Ryck, Siddhartha Mishra, Y. Shang, F. Wang
2024	A*	`conf`	An operator preconditioning perspective on training in physics-informed machine learning. ICLR Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
2024	J	`jnl`	Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. Acta Numer. Tim De Ryck, Siddhartha Mishra
2024	J	`jnl`	Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. CoRR Tim De Ryck, Siddhartha Mishra
2024	J	`jnl`	wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws. SIAM J. Numer. Anal. Tim De Ryck, Siddhartha Mishra, Roberto Molinaro
2023	J	`jnl`	An operator preconditioning perspective on training in physics-informed machine learning. CoRR Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
2023	A*	`conf`	Convolutional Neural Operators for robust and accurate learning of PDEs. NeurIPS Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac
2022	J	`jnl`	Error analysis for deep neural network approximations of parametric hyperbolic conservation laws. CoRR Tim De Ryck, Siddhartha Mishra
2022	J	`jnl`	Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs. Adv. Comput. Math. Tim De Ryck, Siddhartha Mishra
2022	J	`jnl`	Error estimates for physics informed neural networks approximating the Navier-Stokes equations. CoRR Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra
2022	A*	`conf`	Generic bounds on the approximation error for physics-informed (and) operator learning. NeurIPS Tim De Ryck, Siddhartha Mishra
2022	J	`jnl`	Generic bounds on the approximation error for physics-informed (and) operator learning. CoRR Tim De Ryck, Siddhartha Mishra
2022	J	`jnl`	Variable-Input Deep Operator Networks. CoRR Michael Prasthofer, Tim De Ryck, Siddhartha Mishra
2022	J	`jnl`	wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws. CoRR Tim De Ryck, Siddhartha Mishra, Roberto Molinaro
2021	J	`jnl`	Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation. IEEE Trans. Signal Process. Tim De Ryck, Maarten De Vos, Alexander Bertrand
2021	J	`jnl`	Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs. CoRR Tim De Ryck, Siddhartha Mishra
2021	J	`jnl`	On the approximation of functions by tanh neural networks. CoRR Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
2021	J	`jnl`	On the approximation of functions by tanh neural networks. Neural Networks Tim De Ryck, Samuel Lanthaler, Siddhartha Mishra
2020	J	`jnl`	Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation. CoRR Tim De Ryck, Maarten De Vos, Alexander Bertrand
2019	J	`jnl`	On the approximation of rough functions with deep neural networks. CoRR Tim De Ryck, Siddhartha Mishra, Deep Ray