Identify sparse governing equations for dynamical systems from time-series data using PySINDy.
npx skills add https://github.com/pengqianhan/scicomp-skills --skill sindy-skill
Please help me install this Agent Skill. Skill: sindy-skill Source: https://github.com/pengqianhan/scicomp-skills/tree/main/sindy-skill Command: npx skills add https://github.com/pengqianhan/scicomp-skills --skill sindy-skill
This skill addresses the challenge of identifying the underlying mathematical laws governing complex dynamical systems when only time-series data is available.
Use the sindy-skill to analyze the time-series data in data.csv and discover the governing equations with a polynomial degree of 3.
High-intent search queries and answers about installing and using this skill.
To discover governing equations from time-series data, you can use the SINDy algorithm. This skill applies sparse regression via PySINDy to automatically identify the underlying ODEs or PDEs that describe your dynamical system's evolution.
Equation discovery for dynamical systems is the process of identifying underlying mathematical laws from measurement data. This skill uses sparse regression to find the specific differential equations governing a physical experiment or complex system.
Yes, you can use PySINDy to identify differential equations from sensor data. This skill analyzes time-series inputs, like a CSV file, and applies sparse regression to derive the specific ODEs or PDEs describing the system's motion.
To build models for system identification with customizable feature libraries, this skill uses PySINDy. It allows you to apply sparse regression with configurable polynomial degrees and optimizers to construct robust dynamical models.
Yes, this sparse regression tool works with scipy and matplotlib. These dependencies are required alongside PySINDy to perform equation discovery, simulate the discovered models, and visualize the dynamical systems' results.
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