Visualize different modules related to motor control and operations.
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Updated
Apr 19, 2020 - JavaScript
Visualize different modules related to motor control and operations.
Coursework for my Y3 Parallel Scientific Computing I module! Working on parallel physical simulations in C++ using OpenMP for parallelisation.
Simulink model and python interface to simulate electrical motor operations.
mpm_solver
[AAAI24] LE-PDE-UQ endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems.
Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians
[NeurIPS 2022] The implementation for the paper "Learning Physical Dynamics with Subequivariant Graph Neural Networks".
This is our course homework. I create a physical simulation program to demonstrate the dynamics of human hair under the influence of air flow.
PENN code for NeurIPS 2022
Code for the paper "Deep learning of thermodynamics-aware reduced-order models from data" published in Computer Methods in Applied Mechanics and Engineering (CMAME).
Applications of the Teg differentiable programming language to problems spanning graphics and physical simulation.
An extensible benchmark suite to evaluate data-driven physical simulation
[ICLR24] CinDM uses compositional generative models to design boundaries and initial states significantly more complex than the ones seen in training for physical simulation
Code for the paper "Structure-preserving neural networks" published in Journal of Computational Physics (JCP).
Source files for my experiments not limited to computer graphics.
Implementation of CogSci 2019 paper 'Active physical learning via reinforcement learning'
IsoGCN code for ICLR2021
Open source implemention of Moving Particle Semi-implcit (MPS) method
Code for the paper "Thermodynamics-informed graph neural networks" published in IEEE Transactions on Artificial Intelligence (TAI).
Incremental Potential Contact (IPC) is for robust and accurate time stepping of nonlinear elastodynamics. IPC guarantees intersection- and inversion-free trajectories regardless of materials, time-step sizes, velocities, or deformation severity.
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