PyTorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control

Area: Artificial intelligence

CASS member: LEADS

Description

Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER) is an open-source differentiable programming (DP) library for solving parametric constrained optimization problems, physics-informed system identification, and parametric model-based optimal control. NeuroMANCER is written in PyTorch and allows for systematic integration of machine learning with scientific computing for creating end-to-end differentiable models and algorithms embedded with prior knowledge and physics.

Target audience

Science teams interested in parametric constrained optimization and control.

License: BSD-3-Clause

Additional resources