Roberto Molinaro

According to our database, Roberto Molinaro authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

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

Convolutional Neural Operators.

Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bézenac

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Inverse Operators for Solving PDE Inverse Problems.

Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra

Proceedings of the International Conference on Machine Learning, 2023

Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities.

Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2021

Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs.

Genming Bai, Ujjwal Koley, Siddhartha Mishra, Roberto Molinaro

CoRR, 2021

2020

Physics Informed Neural Networks for Simulating Radiative Transfer.

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

Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs.

Siddhartha Mishra, Roberto Molinaro

CoRR, 2020

2019

A Multi-level procedure for enhancing accuracy of machine learning algorithms.

Kjetil O. Lye, Siddhartha Mishra, Roberto Molinaro

CoRR, 2019