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

Hookean springs in PyTorch

Numerical integration methods for mass-springs systems using PyTorch's autodiff

A Python package describing Stochastic Nucleation of Water in vials

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