pysindy
sysidentpy
| pysindy | sysidentpy | |
|---|---|---|
| 5 | 7 | |
| 1,293 | 313 | |
| 2.8% | - | |
| 9.3 | 7.8 | |
| 10 days ago | 26 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pysindy
- Sam Altman's ouster was precipitated by letter to board about AI breakthrough
-
Ask HN: Why don't datacenters have passive rooflines like Net Zero homes?
https://github.com/dynamicslab/pysindy/issues/383#event-1002...
awesome-machine-learning-fluid-mechanics >
- Why are neural networks not multi-dimensional?
- Creating an equation for a system given its data.
-
Physics-Based Deep Learning Book
Not MLP, but PySindy[0] might be a way to find a closed form solution. A simple MLP may not be best way to achieve what are you are after.
[0] https://github.com/dynamicslab/pysindy
sysidentpy
-
Contribuição em biblioteca open source
Link para o site da documentação: SysIdentPy - SysIdentPy
- sysidentpy: A Python Package For System Identification Using NARMAX Models
-
I'm giving out microgrants to open source projects for the third year in a row! Brag about your projects here so I can see them, big or small!
I'm the only maintainer, but I keep including new features (some exclusives, like the algorithm I've developed in my thesis to create NARMAX models), improving the code and documentation, and fixing bugs.
-
A Comparison of Time Series Model Forecasting
benchmark codes: Welcome to SysIdentPy’s documentation! — NARMAX models
-
Criei uma biblioteca open source para previsão de séries temporais
github: wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models (github.com)
-
Help with time series analysis
I built a package where you can build polynomial NARMAX models using the most used method for model selection of this class of models in the beckend. In addition, you can built NARX Neural Networks for forecasting problems (this is built on top of Pytorch) and you can use any model that have a fit/predict method (Catboost, any estimator from sklearn) in a NARX configuration to perform infinity-steps-ahead prediction. Maybe its worth a try. Here is the link of the package: GitHub - wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models
What are some alternatives?
torchsde - Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
neural_prophet - NeuralProphet: A simple forecasting package
Poincare-Maps - MATLAB files for discovery of Poincaré maps
sktime - A unified framework for machine learning with time series
thebe - Turn static HTML pages into live documents with Jupyter kernels.
rav1e - The fastest and safest AV1 encoder.
pbdl-book - Welcome to the Physics-based Deep Learning Book (v0.2)
forge - Lua scriptable build tool
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
space-nerds-in-space - Multi-player spaceship bridge simulator. Captain your starship through adventures with your friends. See https://smcameron.github.io/space-nerds-in-space
Stochastic-Processes - My book: Gentle Introduction to Chaotic Dynamical Systems. Includes stochastic dynamical systems and statistical properties of numeration systems in any dimension.
rpi-rgb-led-matrix - Controlling up to three chains of 64x64, 32x32, 16x32 or similar RGB LED displays using Raspberry Pi GPIO