7 citations · 10 across the 5 of their papers we have counts for
3 papers · 1 filter
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…