16 citations · 46 across the 13 of their papers we have counts for
5 papers · 1 filter
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi +3
Recently, graph neural networks have been gaining a lot of attention to simulate dynamical systems due to their inductive nature leading to zero-shot generalizability. Similarly, p…
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks
Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics. However, traditional approaches require the kn…
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively) encode strong inductive biases that allow them to outperform other models of physical systems significantly…
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu +1
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 encodi…
Unsupervised Graph Neural Network Reveals the Structure--Dynamics Correlation in Disordered Systems
Vaibhav Bihani, Sahil Manchanda, Sayan Ranu +1
Learning the structure--dynamics correlation in disordered systems is a long-standing problem. Here, we use unsupervised machine learning employing graph neural networks (GNN) to i…