peterparity/conservation-laws-manifold-learning
Discovering Conservation Laws using Optimal Transport and Manifold Learning
This project helps physicists, engineers, and applied mathematicians discover underlying conservation laws from raw simulation or experimental data. You input trajectory samples from a dynamical system, and it outputs the conserved quantities inherent in that system. This is ideal for researchers analyzing the fundamental behavior of physical systems.
No commits in the last 6 months.
Use this if you need to identify hidden invariants or conserved properties directly from observed system trajectories without prior knowledge of the governing equations.
Not ideal if you already have a well-defined analytical model for your system and only need to verify known conservation laws.
Stars
22
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 23, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/peterparity/conservation-laws-manifold-learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
pnnl/neuromancer
Pytorch-based framework for solving parametric constrained optimization problems,...
wilsonrljr/sysidentpy
A Python Package For System Identification Using NARMAX Models
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.