REPOGEO REPORT · LITE
pnnl/neuromancer
Default branch master · commit e9456ffa · scanned 5/13/2026, 4:07:01 AM
GitHub: 1,321 stars · 172 forks
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface pnnl/neuromancer, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Emphasize 'framework' and 'end-to-end problem solving' in the README's opening
Why:
CURRENT**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.
COPY-PASTE FIX**Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER)** is an open-source **differentiable programming framework** built on PyTorch, designed for **end-to-end solutions** in parametric constrained optimization, physics-informed system identification, and parametric model-based optimal control. It systematically integrates machine learning with scientific computing to create differentiable models and algorithms embedded with prior knowledge and physics, enabling users to **solve complex problems** that span learning, modeling, control, and optimization.
- mediumreadme#2Add a clear statement about the project's license in the README
Why:
COPY-PASTE FIXAdd a section or line under 'Overview' or 'Getting Started' like: 'NeuroMANCER is distributed under a custom license. Please refer to the `LICENSE.md` file in the repository for full details on terms and conditions.'
- mediumreadme#3Add a 'Comparison to Alternatives' section in the README
Why:
COPY-PASTE FIXAdd a new section to the README, for example: '## Comparison to Alternatives While tools like CVXPY Layers, OptNet, CasADi, and JAX provide powerful components for optimization or differentiable programming, NeuroMANCER distinguishes itself as an integrated framework for building end-to-end solutions. It focuses on combining learning, modeling, and control within a single differentiable environment, specifically tailored for parametric constrained optimization, physics-informed system identification, and model predictive control, rather than just offering individual solvers or general-purpose autodiff.'
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- CVXPY Layers · recommended 1×
- OptNet · recommended 1×
- PyTorch-Opacus · recommended 1×
- DeepOpt · recommended 1×
- GurobiPy · recommended 1×
- CATEGORY QUERYLooking for a PyTorch library to solve parametric constrained optimization problems using deep learning.you: not recommendedAI recommended (in order):
- CVXPY Layers
- OptNet
- PyTorch-Opacus
- DeepOpt
- GurobiPy
- CVXPY
AI recommended 6 alternatives but never named pnnl/neuromancer. This is the gap to close.
Show full AI answer
- CATEGORY QUERYHow can I implement physics-informed model predictive control with differentiable programming in Python?you: not recommendedAI recommended (in order):
- CasADi
- JAX
- PyTorch
- TensorFlow
- Gekko
- do-mpc
AI recommended 6 alternatives but never named pnnl/neuromancer. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of pnnl/neuromancer?passAI named pnnl/neuromancer explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts pnnl/neuromancer in production, what risks or prerequisites should they evaluate first?passAI named pnnl/neuromancer explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo pnnl/neuromancer solve, and who is the primary audience?passAI named pnnl/neuromancer explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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pnnl/neuromancer — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite