On the approximation of rough functions with deep neural networks. - Tim De Ryck; Siddhartha Mishra; Deep Ray (CoRR, 2019)

On the approximation of functions by tanh neural networks. - Tim De Ryck; Samuel Lanthaler; Siddhartha Mishra (Neural Networks, 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)

Generic bounds on the approximation error for physics-informed (and) operator learning. - Tim De Ryck; Siddhartha Mishra (NeurIPS, 2022)

Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs. - Tim De Ryck; Siddhartha Mishra (Adv. Comput. Math., 2022)

Error estimates for physics informed neural networks approximating the Navier-Stokes equations. - Tim De Ryck; Ameya D. Jagtap; Siddhartha Mishra (CoRR, 2022)

Variable-Input Deep Operator Networks. - Michael Prasthofer; Tim De Ryck; Siddhartha Mishra (CoRR, 2022)

Error analysis for deep neural network approximations of parametric hyperbolic conservation laws. - Tim De Ryck; Siddhartha Mishra (CoRR, 2022)

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 (NeurIPS, 2023)

An operator preconditioning perspective on training in physics-informed machine learning. - Tim De Ryck; Florent Bonnet; Siddhartha Mishra; Emmanuel de Bézenac (ICLR, 2024)

Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning. - Tim De Ryck; Siddhartha Mishra (Acta Numer., 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)

Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs. - Tim De Ryck; Siddhartha Mishra; Y. Shang; F. Wang (CoRR, 2025)