REPOGEO REPORT · LITE

greydanus/hamiltonian-nn

Default branch master · commit bcc36223 · scanned 6/1/2026, 12:23:07 PM

GitHub: 528 stars · 158 forks

AI VISIBILITY SCORE

27 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

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 greydanus/hamiltonian-nn, 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.

OVERALL DIRECTION

highreadme#1

Strengthen README's opening statement to clarify core purpose

Why:

CURRENT

Hamiltonian Neural Networks
Sam Greydanus, Misko Dzamba, Jason Yosinski | 2019

COPY-PASTE FIX

Hamiltonian Neural Networks

This PyTorch library implements Hamiltonian Neural Networks (HNNs) to learn and simulate physical dynamics while explicitly preserving energy conservation laws. It's designed for researchers working on physics-informed deep learning, offering models that inherently respect Hamiltonian mechanics for tasks like mass-spring systems, pendulums, and N-body problems.

Sam Greydanus, Misko Dzamba, Jason Yosinski | 2019

highabout#2

Expand the repository description for clarity

Why:

CURRENT

Code for our paper "Hamiltonian Neural Networks"

COPY-PASTE FIX

PyTorch library for Hamiltonian Neural Networks (HNNs) that learn and simulate physical dynamics while preserving energy conservation laws, as presented in our paper.

mediumtopics#3

Add more specific topics to improve categorization

Why:

CURRENT

deep-learning, dynamics, hamiltonian, neural-network, physics, pytorch, research

COPY-PASTE FIX

deep-learning, dynamics, hamiltonian, neural-network, physics, pytorch, research, energy-conservation, physics-informed-ml, scientific-machine-learning, pytorch-library

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.

Recall

0 / 2

0% of queries surface greydanus/hamiltonian-nn

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

google/jax-md

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

google/jax-md · recommended 1×
NVIDIA/modulus · recommended 1×
tensorflow/tensorflow · recommended 1×
keras-team/keras · recommended 1×
pyg-team/pytorch_geometric · recommended 1×

CATEGORY QUERY
How can I apply deep learning to accurately simulate complex physical dynamics?
you: not recommended
AI recommended (in order):
1. JAX-MD (google/jax-md)
2. NVIDIA Modulus (NVIDIA/modulus)
3. TensorFlow (tensorflow/tensorflow)
4. Keras (keras-team/keras)
5. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
6. Deep Graph Library (DGL) (dmlc/dgl)
7. PyTorch (pytorch/pytorch)
8. JAX (google/jax)
9. e3nn (e3nn/e3nn)
10. OpenFOAM
11. FEniCS Project (fenics/fenics)
AI recommended 11 alternatives but never named greydanus/hamiltonian-nn. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a PyTorch library to implement neural networks for learning system Hamiltonians.
you: not recommended
AI recommended (in order):
1. PyTorch Geometric (PyG)
2. DeepMind's JAX
3. PyTorch-Lightning
4. TorchDyn
5. e3nn
6. NeuralODE
7. Spektral
AI recommended 7 alternatives but never named greydanus/hamiltonian-nn. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

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 greydanus/hamiltonian-nn?
pass
AI named greydanus/hamiltonian-nn explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts greydanus/hamiltonian-nn in production, what risks or prerequisites should they evaluate first?
pass
AI did not name greydanus/hamiltonian-nn — likely talking about a different project
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 greydanus/hamiltonian-nn solve, and who is the primary audience?
pass
AI did not name greydanus/hamiltonian-nn — likely talking about a different project
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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MARKDOWN (README)

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite