An energy-based formulation for soft-bodied virtual creatures
A curated collection of Python examples for optimization-based solid simulation, emphasizing algorithmic convergence, penetration-free, and inversion-free conditions, designed for readability and understanding.
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
[SIGGRAPH Asia 2022] Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly
The mdbook source of a free online book on the theory and algorithms of physics-based simulations. You are welcome to make contributions by submitting pull requests or directly contacting the authors.
A free online book on the theory and algorithms of physics-based simulations. To make a contribution, please submit pull requests on the mdbook-src repository (not this one), or directly contact the authors.
A curated set of C++ examples for optimization-based elastodynamic contact simulation using CUDA, emphasizing algorithmic convergence, penetration-free, and inversion-free conditions. Designed for readability and understanding, this tutorial helps beginners learn how to write simple GPU code for efficient solid simulations.
Hookean springs in PyTorch
Numerical integration methods for mass-springs systems using PyTorch's autodiff
Knowledge hub to share and learn the state-of-the-art developments in computer graphics.
A Python package describing Stochastic Nucleation of Water in vials
Uses SFML for a compact physics simulation illustrating elastic collisions in C++.
A simple implementation of mass-spring system for cloth simulation in C++
A framework for physics-based rendering of underwater images using Mitsuba 0.6 This work is part of simulation work done in[1]. [1] Adi Vainiger, Yoav Y. Schechner, Tali Treibitz, Aviad Avni, and David S. Timor, "Optical wide-field tomography of sediment resuspension," Opt. Express 27, A766-A778 (2019) https://opg.optica.org/oe/abstract.cfm?uri=o
Source code used in simulations for the paper "A Framework for Automatic Behavior Generation in Multi-Function Swarms" accepted by Frontiers in Robotics and AI Oct. 2020.
PySB add-on providing utilities to add units to models and perform dimensional analysis.
CPSC 526 final project - fall 2017
Evolutionary algorithm research on a neural network physics-based car controller
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