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Found 16 papers, 4 papers with code
Scaling Face Interaction Graph Networks to Real World Scenes
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design.
Learning 3D Particle-based Simulators from RGB-D Videos
Realistic simulation is critical for applications ranging from robotics to animation.
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.
MultiScale MeshGraphNets
In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-ups over classical solvers whilst maintaining accuracy.
Physical Design using Differentiable Learned Simulators
In our fluid manipulation tasks, the resulting designs outperformed those found by sampling-based optimization techniques.
Predicting Physics in Mesh-reduced Space with Temporal Attention
Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes.
Learned Coarse Models for Efficient Turbulence Simulation
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.
Constraint-based graph network simulator
We can improve the simulation accuracy on a larger system by applying more solver iterations at test time.
Learning ground states of quantum Hamiltonians with graph networks
In this work we use graph neural networks to define a structured variational manifold and optimize its parameters to find high quality approximations of the lowest energy solutions on a diverse set of Heisenberg Hamiltonians.
Learned Simulators for Turbulence
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics.
Learning Mesh-Based Simulation with Graph Networks
Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation.
Learning to Simulate Complex Physics with Graph Networks
Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another.
Combining Q-Learning and Search with Amortized Value Estimates
In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values.
Visual Imitation with a Minimal Adversary
The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators.
Playing hard exploration games by watching YouTube
One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator.
