Tim-Salzmann/l4casadi
Use PyTorch Models with CasADi for data-driven optimization or learning-based optimal control. Supports Acados.
This tool helps engineers and researchers integrate machine learning models, specifically those built with PyTorch, into optimization frameworks like CasADi. It allows you to use your trained PyTorch models to define complex system dynamics or cost functions within optimal control and data-driven optimization problems. The input is a traceable and differentiable PyTorch model, and the output is a highly efficient, potentially hardware-accelerated component for numerical optimization.
560 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to embed data-driven insights from PyTorch models directly into robust numerical optimization routines for control systems or complex planning tasks.
Not ideal if your primary goal is general-purpose machine learning model deployment without a strong need for integrated numerical optimization or optimal control.
Stars
560
Forks
46
Language
Python
License
MIT
Category
Last pushed
Jun 05, 2025
Commits (30d)
0
Dependencies
3
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