7 citations · 10 across the 5 of their papers we have counts for
5 papers
Rapid training of Hamiltonian graph networks using random features
Atamert Rahma, Chinmay Datar, Ana Cukarska +1
Learning dynamical systems that respect physical symmetries and constraints remains a fundamental challenge in data-driven modeling. Integrating physical laws with graph neural net…
Training Hamiltonian neural networks without backpropagation
Atamert Rahma, Chinmay Datar, Felix Dietrich
Neural networks that synergistically integrate data and physical laws offer great promise in modeling dynamical systems. However, iterative gradient-based optimization of network p…
Fast training of accurate physics-informed neural networks without gradient descent
Chinmay Datar, Taniya Kapoor, Abhishek Chandra +6
Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a p…
Systematic construction of continuous-time neural networks for linear dynamical systems
Chinmay Datar, Adwait Datar, Felix Dietrich +1
Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation thr…
Sampling weights of deep neural networks
Erik Lien Bolager, Iryna Burak, Chinmay Datar +2
We introduce a probability distribution, combined with an efficient sampling algorithm, for weights and biases of fully-connected neural networks. In a supervised learning context,…