# List of publications

## Publications

- T. De Ryck, F. Bonnet, S. Mishra and E. de Bézenac. An operator preconditioning perspective on training in physics-informed machine learning. Accepted to
*ICLR*, 2024.
- T. De Ryck, S. Mishra and R. Molinaro. wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws.
*SIAM Journal on Numerical Analysis*, 2024.
- B. Raonić, R. Molinaro, T. De Ryck, T. Rohner, F. Bartolucci, R. Alaifari, S. Mishra and E. de Bézenac. Convolutional Neural Operators for robust and accurate learning of PDEs. Accepted to
*NeurIPS*, 2023.
- T. De Ryck, A. D. Jagtap and S. Mishra. Error estimates for physics informed neural networks approximating the Navier-Stokes equations.
*IMA Journal of Numerical Analysis*, 2023.
- T. De Ryck and S. Mishra. Error analysis for deep neural network approximations of parametric hyperbolic conservation laws.
*Mathematics of Computation*, 2023.
- T. De Ryck and S. Mishra, Generic bounds on the approximation error for physics-informed (and) operator learning. Accepted to
*NeurIPS*, 2022.
- T. De Ryck and S. Mishra. Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs.
*Advances in Computational Mathematics*, 2022.
- T. De Ryck, S. Mishra and D. Ray. On the approximation of rough functions with deep neural networks.
*SeMA Journal*, 2022.
- T. De Ryck, S. Lanthaler and S. Mishra. On the approximation of functions by tanh neural networks.
*Neural Networks*, 2021.
- T. De Ryck, M. De Vos and A. Bertrand. Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation.
*IEEE Transactions on Signal Processing*, 2021.

## Preprints

## Master thesis