pnnl/neuromancer
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
This tool helps scientists and engineers in fields like energy systems or chemical processes design and optimize complex systems that involve both physical laws and learned behaviors. You input data describing the system's behavior and desired outcomes, and it outputs a model that can predict system performance, optimize control strategies, or identify unknown physical parameters, even under real-world constraints. It's designed for professionals working on control, modeling, or optimization tasks.
1,295 stars. Available on PyPI.
Use this if you need to integrate machine learning with scientific computing to solve problems like optimizing building energy use, controlling fluid dynamics, or identifying parameters in physical systems, especially when physical constraints are important.
Not ideal if your problem is purely data-driven without any underlying physical laws or explicit constraints you need to embed.
Stars
1,295
Forks
171
Language
Python
License
—
Category
Last pushed
Mar 06, 2026
Commits (30d)
0
Dependencies
20
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