no code implementations • 11 Jul 2023 • Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, N M Anoop Krishnan
Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory.
no code implementations • 20 Jun 2023 • Suresh Bishnoi, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan
Here, we propose a framework, namely Brownian graph neural networks (BROGNET), combining stochastic differential equations (SDEs) and GNNs to learn Brownian dynamics directly from the trajectory.
1 code implementation • 10 Nov 2022 • Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N M Anoop Krishnan, Sayan Ranu
We evaluate these models on spring, pendulum, gravitational, and 3D deformable solid systems to compare the performance in terms of rollout error, conserved quantities such as energy and momentum, and generalizability to unseen system sizes.
no code implementations • 19 Oct 2022 • Suresh Bishnoi, Skyler Badge, Jayadeva, N. M. Anoop Krishnan
In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set.
no code implementations • 22 Sep 2022 • Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Neural networks with physics based inductive biases such as Lagrangian neural networks (LNN), and Hamiltonian neural networks (HNN) learn the dynamics of physical systems by encoding strong inductive biases.