NeuroMANCER is a framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control using deep learning. It integrates machine learning with scientific computing by embedding physics equations, domain knowledge, and constraints into end-to-end differentiable models and algorithms. The library provides tools for Learning To Optimize, Learning To Model, and Learning To Control tasks using neural network components and symbolic programming.