Abstract: In this work, we assess the ability of physics-informed neural networks (PINNs) to solve increasingly-complex coupled ordinary differential equations (ODEs). We focus on a pair of benchmarks ...

NOTE: This project was made for educational purposes (mainly for me to learn how a physics engine works), and therefore it is not recommended to use this library in production. Consider using other 2D ...

We need to set up a continuous integration and deployment pipeline for 2D-Physics-Sandbox. I suggest using GitHub Actions.

A single equation found on a Babylonian clay tablet, written nearly four millennia ago, reveals a mathematical insight eerily similar to theories used in modern ...

Abstract: Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh ...

Einstein’s theory of relativity reshaped our understanding of time space and gravity But deep inside its equations lies a mystery the one Einstein never predicted a hole that could change how physics ...