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Found 5 papers, 1 papers with code
REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics
Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces.
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks
Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice.
Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks
Continuum mechanics simulators, numerically solving one or more partial differential equations, are essential tools in many areas of science and engineering, but their performance often limits application in practice.
Simulating Surface Wave Dynamics with Convolutional Networks
We investigate the performance of fully convolutional networks to simulate the motion and interaction of surface waves in open and closed complex geometries.
Comparing recurrent and convolutional neural networks for predicting wave propagation
Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering.
