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Found 5 papers, 1 papers with code
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory.
Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics
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.
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
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.
Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors
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.
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
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.
