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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
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.
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.
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.
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.
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 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