ogaken1104

cibook-study-python

効果検証入門のコードをPythonで実装しました。

jax-autovmap

Automatically apply JAX vmap given base dimensions of inputs

cyjax

Machine learning Calabi-Yau metrics with JAX

normalizing-flows

PyTorch implementation of normalizing flow models

ml4p

Machine learning for physicists

samplemod

GPy

Gaussian processes framework in python

deeponet

Learning nonlinear operators via DeepONet

deeponet-fno

DeepONet & FNO (with practical extensions)

neuraloperator

Learning in infinite dimension with neural operators.

DataDrivenDiffEq.jl

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

pystokes

PyStokes: phoresis and Stokesian hydrodynamics in Python. github.com/rajeshrinet/pystokes

torchdiffeq

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

pinn-torch

PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations

idrlnet

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

NonLinPDEs-GPsolver

Code for the paper: Solving and Learning Nonlinear PDEs with Gaussian Processes

linpde-gp

Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"

HPM

Hidden physics models: Machine learning of nonlinear partial differential equations

PDE_GP

Machine learning of linear differential equations using Gaussian processes

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

simulai

A toolkit with data-driven pipelines for physics-informed machine learning.

HetMOGP

Heterogeneous Multi-output Gaussian Processes

phd-thesis

I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth

dolfinx

Next generation FEniCS problem solving environment

linear_operator

A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch

NumericalGP

Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations

python_stat_sample

research

PINNs

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