3 code implementations • 29 May 2024 • Maximilian Herde, Bogdan Raonić, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra

Moreover, Poseidon scales with respect to model and data size, both for pretraining and for downstream tasks.

2 code implementations • NeurIPS 2023 • Bogdan Raonić, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac

Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs.

no code implementations • 26 Jan 2023 • Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra

A large class of inverse problems for PDEs are only well-defined as mappings from operators to functions.

no code implementations • 3 Oct 2022 • Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra

A large class of hyperbolic and advection-dominated PDEs can have solutions with discontinuities.

no code implementations • 18 Jul 2022 • Tim De Ryck, Siddhartha Mishra, Roberto Molinaro

Physics informed neural networks (PINNs) require regularity of solutions of the underlying PDE to guarantee accurate approximation.

1 code implementation • 25 Sep 2020 • Siddhartha Mishra, Roberto Molinaro

We propose a novel machine learning algorithm for simulating radiative transfer.

no code implementations • 29 Jun 2020 • Siddhartha Mishra, Roberto Molinaro

Physics informed neural networks (PINNs) have recently been widely used for robust and accurate approximation of PDEs.

no code implementations • 29 Jun 2020 • Siddhartha Mishra, Roberto Molinaro

Physics informed neural networks (PINNs) have recently been very successfully applied for efficiently approximating inverse problems for PDEs.

1 code implementation • 20 Sep 2019 • Kjetil O. Lye, Siddhartha Mishra, Roberto Molinaro

We propose a multi-level method to increase the accuracy of machine learning algorithms for approximating observables in scientific computing, particularly those that arise in systems modeled by differential equations.

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