REPOGEO REPORT · LITE
thunil/Physics-Based-Deep-Learning
Default branch master · commit b901b50c · scanned 5/25/2026, 9:53:06 AM
GitHub: 1,888 stars · 315 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 thunil/Physics-Based-Deep-Learning, 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#1Reposition the README's opening to clarify it's a resource collection, not a library
Why:
CURRENTThe following collection of materials targets _"Physics-Based Deep Learning"_ (PBDL), i.e., the field of methods with combinations of physical modeling and deep learning (DL) techniques.
COPY-PASTE FIXThis repository is a curated collection of materials and resources for _"Physics-Based Deep Learning"_ (PBDL), focusing on methods that combine physical modeling and deep learning (DL) techniques. It serves as a comprehensive guide and educational resource, including links to our digital PBDL book.
- hightopics#2Add relevant topics to the repository
Why:
COPY-PASTE FIXphysics-based-deep-learning, pbdl, physics-informed-neural-networks, pinn, scientific-machine-learning, sciml, deep-learning, physics, computational-physics, inverse-problems, forward-simulations, research-collection, educational-resource
- highlicense#3Add a LICENSE file to the repository
Why:
COPY-PASTE FIXAdd a `LICENSE` file to the repository root with the MIT License text.
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.
- DeepXDE · recommended 1×
- NVIDIA Modulus · recommended 1×
- SciANN · recommended 1×
- NeuralPDE.jl · recommended 1×
- PyTorch-Opacus · recommended 1×
- CATEGORY QUERYHow to integrate physical models with neural networks for scientific simulations?you: not recommendedAI recommended (in order):
- DeepXDE
- NVIDIA Modulus
- SciANN
- NeuralPDE.jl
- PyTorch-Opacus
- TensorFlow
- FEniCS
AI recommended 7 alternatives but never named thunil/Physics-Based-Deep-Learning. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat frameworks exist for solving inverse problems using deep learning techniques?you: not recommendedAI recommended (in order):
- DeepInverse
- PyTorch-Lightning
- TensorFlow (with Keras)
- Deep Learning for Inverse Problems (DLIP) Toolbox
- Modulus (NVIDIA)
- JAX (with Flax/Haiku)
AI recommended 6 alternatives but never named thunil/Physics-Based-Deep-Learning. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenessfail
Suggestion:
- 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 thunil/Physics-Based-Deep-Learning?passAI named thunil/Physics-Based-Deep-Learning explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts thunil/Physics-Based-Deep-Learning in production, what risks or prerequisites should they evaluate first?passAI named thunil/Physics-Based-Deep-Learning 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 thunil/Physics-Based-Deep-Learning solve, and who is the primary audience?passAI did not name thunil/Physics-Based-Deep-Learning — 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
Drop this badge into the README of thunil/Physics-Based-Deep-Learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/thunil/Physics-Based-Deep-Learning)<a href="https://repogeo.com/en/r/thunil/Physics-Based-Deep-Learning"><img src="https://repogeo.com/badge/thunil/Physics-Based-Deep-Learning.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
thunil/Physics-Based-Deep-Learning — 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