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oxsa

Toolbox for developing pipelines for analysis of spectroscopy data. Includes code for loading Siemens spectroscopy data, and for spectral fitting/analysis.

padam

Partially Adaptive Momentum Estimation

papers-we-love

Papers from the computer science community to read and discuss.

paragraph2vec

parametric-t-sne

Running parametric t-SNE by Laurens Van Der Maaten with Octave and oct2py.

pararealml

A machine learning boosted parallel-in-time differential equation solver framework.

path-space-pde-solver

pattern_classification

A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks

pau

Padé Activation Units: End-to-end Learning of Activation Functions in Deep Neural Network

pbdl-book

Welcome to the Physics-based Deep Learning Book (v0.1)

pbt-nmt

pde-backward-solver

pde-constrained-optimization-pi-deeponet

pde-dataset-generator

A tool for generating PDEs ground truth datasets from ARCSim, FEniCS and SU2

pde-deeponet-learning

A Deep Learning Approach to Solving PDEs: Implementing Neural Networks with Pytorch and Jax

pde-solvers

PDE Solvers: FEM vs Deep Learning

pde-surrogate

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

pde_gp

Machine learning of linear differential equations using Gaussian processes

pdelearning

Code repository for the paper "Learning partial differential equations for biological transport models from noisy spatiotemporal data"

pdesbynns

This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means of neural networks using TensorFlow.

pecann

Pyhsics and Equality Constrained Artificial Neural Networks

phd_pinns

phynet

PhyNet: Physics Guided Neural Networksfor Particle Drag Force Prediction in Assembly

physics-based-deep-learning

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

physics-informed-autoencoders

Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid flow dynamics

physics-informed-deep-learning

NASA Physics-Informed Deep Learning Project

physics-informed-deeponets

physics-informed-neural-networks

Investigating PINNs

physics-informed-neural-networks-1

continuous_time_identification (Navier-Stokes)

physics-informed-neural-networks-2

A generalization of the physics informed neural network framework to solve partial differential equations.