Python programs, usually short, of considerable difficulty, to perfect particular skills.

'Random Features for Large-Scale Kernel Machines' by Ali Rahimi and Benjamin Recht. The code within provides a detailed walkthrough of the techniques introduced in the paper, demonstrating how to e…

This repository implements the 2022 paper 'Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data' by Tanaka et al.

Automated Detection of Military Vehicles from Video Input

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

Convert PDF to markdown + JSON quickly with high accuracy

DeepONet & FNO (with practical extensions)

U-FNO - an enhanced Fourier neural operator-based deep-learning model for multiphase flow

PDEBench: An Extensive Benchmark for Scientific Machine Learning

The unofficial python package that returns response of Google Bard through cookie value.

Notebook to go along with a lecture for the MIT course 8.16: Data Science in Physics on neural simulation-based inference.

Machine Learning for Physics and Astronomy Learning Check-ins

In these notebooks we will see how we can use physics laws and small amounts of data to solve partial differential equation such as Poisson, Burger, and others using Neural Network. The first part …

Neural network based solvers for partial differential equations and inverse problems 🌌. Implementation of physics-informed neural networks in pytorch.

Simple PyTorch Implementation of Physics Informed Neural Network (PINN)

The Mojo Programming Language

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

Code for the paper - "Bayesian deep learning for spatial interpolation in the presence of auxiliary information"

Learning in infinite dimension with neural operators.

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

A library for scientific machine learning and physics-informed learning

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

A place to share problems solved with SciANN

2D incompressible fluid solver implemented in Taichi.

🦜🔗 Build context-aware reasoning applications

uBlock Origin - An efficient blocker for Chromium and Firefox. Fast and lean.