A Multi-level procedure for enhancing accuracy of machine learning algorithms. - Kjetil O. Lye; Siddhartha Mishra; Roberto Molinaro (CoRR, 2019)

Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs. - Siddhartha Mishra; Roberto Molinaro (CoRR, 2020)

Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs II: A class of inverse problems. - Siddhartha Mishra; Roberto Molinaro (CoRR, 2020)

Physics Informed Neural Networks for Simulating Radiative Transfer. - Siddhartha Mishra; Roberto Molinaro (CoRR, 2020)

Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs. - Genming Bai; Ujjwal Koley; Siddhartha Mishra; Roberto Molinaro (CoRR, 2021)

Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. - Samuel Lanthaler; Roberto Molinaro; Patrik Hadorn; Siddhartha Mishra (ICLR, 2023)

Neural Inverse Operators for Solving PDE Inverse Problems. - Roberto Molinaro; Yunan Yang; Björn Engquist; Siddhartha Mishra (ICML, 2023)

Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning. - Francesca Bartolucci; Emmanuel de Bézenac; Bogdan Raonic; Roberto Molinaro; Siddhartha Mishra; Rima Alaifari (NeurIPS, 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 (NeurIPS, 2023)

Convolutional Neural Operators. - Bogdan Raonic; Roberto Molinaro; Tobias Rohner; Siddhartha Mishra; Emmanuel de Bézenac (CoRR, 2023)

Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning. - Francesca Bartolucci; Emmanuel de Bézenac; Bogdan Raonic; Roberto Molinaro; Siddhartha Mishra; Rima Alaifari (CoRR, 2023)

Poseidon: Efficient Foundation Models for PDEs. - Maximilian Herde; Bogdan Raonic; Tobias Rohner; Roger Käppeli; Roberto Molinaro; Emmanuel de Bézenac; Siddhartha Mishra (NeurIPS, 2024)

Generative AI for fast and accurate Statistical Computation of Fluids. - Roberto Molinaro; Samuel Lanthaler; Bogdan Raonic; Tobias Rohner; Victor Armegioiu; Zhong Yi Wan; Fei Sha; Siddhartha Mishra; Leonardo Zepeda-Núñez (CoRR, 2024)

EPT-1.5 Technical Report. - Roberto Molinaro; Jordan Dane Daubinet; Alexander Jakob Dautel; Andreas Schlueter; Alex Grigoryev; Nikoo Ekhtiari; Bas Steunebrink; Kevin Thiart; Roan John Song; Henry Martin; Leonie Wagner; Andrea Giussani; et al. (CoRR, 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)

EPT-2 Technical Report. - Roberto Molinaro; Niall Siegenheim; Niels Poulsen; Jordan Dane Daubinet; Henry Martin; Mark Frey; Kevin Thiart; Alexander Jakob Dautel; Andreas Schlueter; Alex Grigoryev; Bogdan Danciu; Nikoo Ekhtiari; et al. (CoRR, 2025)