Computational Physics

This page is a tiny collection of examples of and special materials about computational physics. Basically, a bunch of additional (impressive) things related to computational physics that are hard to categorize any further (at least for me). Of course, this is not even 1% of all of the things that exist out there, only something I have stumbled upon and saved.

YouTube Channels

IPAM - Institute For Pure & Applied Mathematics (YouTube). This is a great actively updated channel with lecture and presentations on many topics that one can consider computational physics.
Mr. P. Solver is a YouTube channel with tutorials on computational physics.
Steve Brunton is a professor of Applied Mathematics. His YouTube channel is full of both physical and computational educational materials.
Brian Douglas makes awesome lectures on Control Systems, in which you might become interested as well.
The Julia Programming Language (YouTube). There happen to be some physics-related talks posted here as well.
Christopher Rackauckas (one of the main figures in scientific programming for Julia) used to post some talks on his YouTube. They are still interesting to watch.
Jousef Murad mostly posts podcasts about engineering but you ma find many of his guests and himself quite inspiring in terms of science. You may also like his other channel.
Statistical Physics of Machine Learning - archived lectures of the summer school in Les Houches, July 4 - 29, 2022

Single Videos

Bayesian Deep Learning on a Quantum Computer
Evolving Wind Turbine Blades
Ising Model Monte Carlo Simulation
The relationship between chaos, fractal and physics

Other

FastJet is a well-documented C++ library for jet reconstruction algorithms (used in high energy physics to analyse data that comes from collider experiments). I will write a separate page on this topic (but maybe not about FastJet in particular) in Ukrainian and maybe in English some day.
LiquidFun is a 2D rigid-body and fluid simulation C++ library made by Google.
Learning to Simulate Complex Physics with Graph Networks - a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another
Momentum Computing
Physics Based Deep Learning - a collection of links. See also this page.