167 citations · 1.3k across the 78 of their papers we have counts for
11 papers · 1 filter
A universal approximation theorem for nonlinear resistive networks
Benjamin Scellier, Siddhartha Mishra
Resistor networks have recently been studied as analog computing platforms for machine learning, particularly due to their compatibility with the Equilibrium Propagation training f…
An operator preconditioning perspective on training in physics-informed machine learning
Tim De Ryck, Florent Bonnet, Siddhartha Mishra +1
In this paper, we investigate the behavior of gradient descent algorithms in physics-informed machine learning methods like PINNs, which minimize residuals connected to partial dif…
Multilevel domain decomposition-based architectures for physics-informed neural networks
Victorita Dolean, Alexander Heinlein, Siddhartha Mishra +1
Physics-informed neural networks (PINNs) are a powerful approach for solving problems involving differential equations, yet they often struggle to solve problems with high frequenc…
How does over-squashing affect the power of GNNs?
Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein +4
Graph Neural Networks (GNNs) are the state-of-the-art model for machine learning on graph-structured data. The most popular class of GNNs operate by exchanging information between…
Neural Oscillators are Universal
Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
Coupled oscillators are being increasingly used as the basis of machine learning (ML) architectures, for instance in sequence modeling, graph representation learning and in physica…
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonić +3
Recently, operator learning, or learning mappings between infinite-dimensional function spaces, has garnered significant attention, notably in relation to learning partial differen…