pnnl/neuromancer

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1369 stars · 188 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NeuroMANCER is a PyTorch-based differentiable programming library for solving parametric constrained optimization problems, physics-informed system identification, and parametric model-based optimal control. It provides a symbolic programming interface for embedding physics equations, domain knowledge, and constraints into end-to-end differentiable machine learning models.

Use cases

• solve parametric constrained optimization problems with neural networks
• perform physics-informed system identification of dynamical systems
• learn neural controllers via differentiable predictive control
• model fluid dynamics with physics-informed neural networks
• train differentiable optimization layers with safety constraints
• learn Koopman operators or SINDy models of nonlinear dynamics
• optimize building HVAC control with learned models

When to choose

• you need differentiable constrained optimization embedded in PyTorch training loops
• you want to combine scientific computing and machine learning with physics priors
• you are researching learning-to-optimize, learning-to-model, or learning-to-control methods
• you need parametric model predictive control for nonlinear systems

When to avoid

• you need a general-purpose deep learning framework without optimization or control focus
• you require guaranteed optimal solutions to hard constrained problems rather than learned approximations
• you work outside Python/PyTorch ecosystems

Facets

library · maturity active

machine-learning deep-learning simulation math machine-learning deep-learning python cross-platform differentiable-programming constrained-optimization model-predictive-control physics-informed-neural-networks system-identification pytorch scientific-machine-learning control-systems algorithms automation

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