Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs.
CoRR, January, 2025
wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws.
SIAM J. Numer. Anal., 2024
Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning.
Acta Numer., 2024
An operator preconditioning perspective on training in physics-informed machine learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Convolutional Neural Operators for robust and accurate learning of PDEs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws.
CoRR, 2022
Variable-Input Deep Operator Networks.
CoRR, 2022
Error estimates for physics informed neural networks approximating the Navier-Stokes equations.
CoRR, 2022
Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs.
Adv. Comput. Math., 2022
Generic bounds on the approximation error for physics-informed (and) operator learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation.
IEEE Trans. Signal Process., 2021
On the approximation of functions by tanh neural networks.
Neural Networks, 2021
On the approximation of rough functions with deep neural networks.
CoRR, 2019