Popular GitHub Repositories for Partial Evaluators

Learning in infinite dimension with neural operators.

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

Benchmark datasets, data loaders, and evaluators for graph machine learning

Scaled-YOLOv4: Scaling Cross Stage Partial Network

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Unofficial implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions". Try at: www.fixmyphoto.ai

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' [Liu+, ECCV2018]

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

This is a ChatGPT based prompt generation model for MidJorney. The purpose of this model is to simplify the creation of images and increase their creativity. By introducing a partial hint, ChatGPT creates a follow-up that can be used to stimulate creativity and provide new ideas.

[ICCV 2023] You Only Look at One Partial Sequence

Graph Neural PDEs

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

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

18.S096 - Applications of Scientific Machine Learning

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax for Partial Differential Equations

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations