A review of graph neural network applications in mechanics-related domains
Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …
and physical representations, particularly for non-uniform structures. Graph neural networks …
Learning rigid dynamics with face interaction graph networks
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex
geometry and the strong non-linearity of the interactions. While graph neural network (GNN) …
geometry and the strong non-linearity of the interactions. While graph neural network (GNN) …
Learning articulated rigid body dynamics with lagrangian graph neural network
Lagrangian and Hamiltonian neural networks LNN and HNNs, respectively) encode strong
inductive biases that allow them to outperform other models of physical systems significantly …
inductive biases that allow them to outperform other models of physical systems significantly …
MTP-GO: Graph-based probabilistic multi-agent trajectory prediction with neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of surrounding
road users' future behavior. In response to this need and the associated challenges, we …
road users' future behavior. In response to this need and the associated challenges, we …
Learning iterative reasoning through energy minimization
Deep learning has excelled on complex pattern recognition tasks such as image
classification and object recognition. However, it struggles with tasks requiring nontrivial …
classification and object recognition. However, it struggles with tasks requiring nontrivial …
Learning flexible body collision dynamics with hierarchical contact mesh transformer
Recently, many mesh-based graph neural network (GNN) models have been proposed for
modeling complex high-dimensional physical systems. Remarkable achievements have …
modeling complex high-dimensional physical systems. Remarkable achievements have …
A physical-information-flow-constrained temporal graph neural network-based simulator for granular materials
This paper introduces the Temporal Graph Neural Network-based Simulator (TGNNS), a
novel physical-information-flow-constrained deep learning-based simulator for granular …
novel physical-information-flow-constrained deep learning-based simulator for granular …
A composable machine-learning approach for steady-state simulations on high-resolution grids
In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable
Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher …
Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher …
Inferring relational potentials in interacting systems
Abstract Systems consisting of interacting agents are prevalent in the world, ranging from
dynamical systems in physics to complex biological networks. To build systems which can …
dynamical systems in physics to complex biological networks. To build systems which can …
Graph Neural Network-based surrogate model for granular flows
Accurate simulation of granular flow dynamics is crucial for assessing geotechnical risks,
including landslides and debris flows. Traditional numerical methods are limited by their …
including landslides and debris flows. Traditional numerical methods are limited by their …