RRepoGEO

REPOGEO REPORT · LITE

WassimTenachi/PhySO

Default branch main · commit bfbfa88d · scanned 5/16/2026, 5:46:48 PM

GitHub: 1,967 stars · 266 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /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
3 / 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 WassimTenachi/PhySO, 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
    Reposition README opening to emphasize physical law discovery and physics-informed approach

    Why:

    CURRENT
    Physical symbolic optimization ( $\Phi$-SO ) - A symbolic optimization package built for physics.
    COPY-PASTE FIX
    PhySO (Physical Symbolic Optimization) is a Python package that leverages deep reinforcement learning and physics-informed constraints to discover interpretable analytical physical laws and equations directly from experimental data.
  • mediumtopics#2
    Add specific topics for physics-informed AI and dimensional analysis

    Why:

    CURRENT
    deep-learning, equation-discovery, machine-learning, physics, python, reinforcement-learning, symbolic-regression
    COPY-PASTE FIX
    deep-learning, dimensional-analysis, equation-discovery, machine-learning, physics, physics-informed-ai, python, reinforcement-learning, symbolic-regression
  • lowreadme#3
    Update 'What's New' section with current or past release information

    Why:

    CURRENT
    What's New ✨  **2025-08** : 📦 Install via `pip install physo` and `conda` now available!  **2025-07** : 🐍 Python 3.12 + latest `NumPy`/`PyTorch`/`SymPy` support.  **2024-06** : 📚 Full documentation overhaul.  **2024-05** : 🔬 **Class SR**: Multi-dataset symbolic regression.  **2024-02** : 🎯 Uncertainty-aware fitting.  **2023-08** : ⚡ Dimensional analysis acceleration.  **2023-03** : 🌟 **PhySO** initial release (physics-focused SR).
    COPY-PASTE FIX
    What's New ✨  **2024-05** : 🔬 **Class SR**: Multi-dataset symbolic regression.  **2024-02** : 🎯 Uncertainty-aware fitting.  **2023-08** : ⚡ Dimensional analysis acceleration.  **2023-03** : 🌟 **PhySO** initial release (physics-focused SR).

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 WassimTenachi/PhySO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 3×
  2. TensorFlow · recommended 3×
  3. PySINDy · recommended 1×
  4. DeepONet · recommended 1×
  5. DeepXDE · recommended 1×
  • CATEGORY QUERY
    How can I discover underlying physical equations from experimental data using AI?
    you: not recommended
    AI recommended (in order):
    1. PySINDy
    2. DeepONet
    3. DeepXDE
    4. PyTorch
    5. TensorFlow
    6. gplearn
    7. scikit-learn-genetic
    8. AI Feynman
    9. torchdiffeq
    10. tf.keras.layers.ODEBlock

    AI recommended 10 alternatives but never named WassimTenachi/PhySO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries perform symbolic regression to infer scientific formulas with deep learning?
    you: not recommended
    AI recommended (in order):
    1. PySR
    2. Deep Symbolic Regression
    3. PyTorch
    4. TensorFlow
    5. GP-GNN
    6. SymPy
    7. PyTorch
    8. TensorFlow

    AI recommended 8 alternatives but never named WassimTenachi/PhySO. 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 WassimTenachi/PhySO?
    pass
    AI named WassimTenachi/PhySO explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts WassimTenachi/PhySO in production, what risks or prerequisites should they evaluate first?
    pass
    AI named WassimTenachi/PhySO 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 WassimTenachi/PhySO solve, and who is the primary audience?
    pass
    AI named WassimTenachi/PhySO explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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WassimTenachi/PhySO — 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