Zheng-Meng/Parameter-Tracking-with-Machine-Learning
Codes for ''Machine-learning parameter tracking with partial state observation'', which published in Physical Review Research.
This project helps scientists track how key parameters change over time in complex, dynamic systems, even when they can only observe a small part of the system's overall behavior. You input partial measurements of a system's state, and it outputs a real-time estimation of the parameter's value. This is ideal for researchers studying fields like ecology, climate science, or engineering, where underlying conditions might be shifting.
No commits in the last 6 months.
Use this if you need to monitor how a specific parameter is evolving within a chaotic or nonlinear system, and you only have access to incomplete measurement data, without needing historical parameter values.
Not ideal if your system parameters are constant or if you have complete state observations and historical parameter data readily available for direct analysis.
Stars
7
Forks
1
Language
MATLAB
License
MIT
Category
Last pushed
Apr 02, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Zheng-Meng/Parameter-Tracking-with-Machine-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.