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Journal Articles
- 2024
[j1]Tim De Ryck
, Siddhartha Mishra, Roberto Molinaro:
wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws. SIAM J. Numer. Anal. 62(2): 811-841 (2024)
Conference and Workshop Papers
- 2023
- [c4]Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra:
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. ICLR 2023 - [c3]Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra:
Neural Inverse Operators for Solving PDE Inverse Problems. ICML 2023: 25105-25139 - [c2]Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari:
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning. NeurIPS 2023 - [c1]Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac:
Convolutional Neural Operators for robust and accurate learning of PDEs. NeurIPS 2023
Informal and Other Publications
- 2024
- [i12]Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra:
Poseidon: Efficient Foundation Models for PDEs. CoRR abs/2405.19101 (2024) - [i11]Roberto Molinaro, Samuel Lanthaler, Bogdan Raonic, Tobias Rohner, Victor Armegioiu, Zhong Yi Wan, Fei Sha, Siddhartha Mishra, Leonardo Zepeda-Núñez:
Generative AI for fast and accurate Statistical Computation of Fluids. CoRR abs/2409.18359 (2024) - 2023
- [i10]Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra:
Neural Inverse Operators for Solving PDE Inverse Problems. CoRR abs/2301.11167 (2023) - [i9]Bogdan Raonic, Roberto Molinaro, Tobias Rohner, Siddhartha Mishra, Emmanuel de Bézenac:
Convolutional Neural Operators. CoRR abs/2302.01178 (2023) - [i8]Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari:
Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning. CoRR abs/2305.19913 (2023) - 2022
- [i7]Tim De Ryck, Siddhartha Mishra, Roberto Molinaro:
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws. CoRR abs/2207.08483 (2022) - [i6]Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra:
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. CoRR abs/2210.01074 (2022) - 2021
- [i5]Genming Bai, Ujjwal Koley, Siddhartha Mishra, Roberto Molinaro:
Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs. CoRR abs/2104.05584 (2021) - 2020
- [i4]Siddhartha Mishra, Roberto Molinaro:
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs. CoRR abs/2006.16144 (2020) - [i3]Siddhartha Mishra, Roberto Molinaro:
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs II: A class of inverse problems. CoRR abs/2007.01138 (2020) - [i2]Siddhartha Mishra, Roberto Molinaro:
Physics Informed Neural Networks for Simulating Radiative Transfer. CoRR abs/2009.13291 (2020) - 2019
- [i1]Kjetil O. Lye, Siddhartha Mishra, Roberto Molinaro:
A Multi-level procedure for enhancing accuracy of machine learning algorithms. CoRR abs/1909.09448 (2019)
Coauthor Index
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