forgi86/sysid-transformers
Code to reproduce the results of the paper In-context learning for model-free system identification
This project helps control engineers and system designers to predict how dynamic systems will behave without needing to build a specific mathematical model for each one. You provide it with a history of system inputs and outputs, and it uses that 'context' to predict future outputs. This is ideal for professionals working with various dynamic systems, such as in robotics, process control, or aerospace, who need quick and accurate predictions.
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Use this if you need to understand or predict the behavior of a wide range of dynamic systems, like industrial processes or autonomous vehicles, using historical data rather than developing a new model for every unique system.
Not ideal if you already have highly accurate, well-defined mathematical models for all your systems or if you require explicit, interpretable system equations for regulatory or design purposes.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jul 31, 2024
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