RRepoGEO

REPOGEO REPORT · LITE

WassimTenachi/PhySO

Default branch main · commit bfbfa88d · scanned 6/27/2026, 3:21:44 PM

GitHub: 1,966 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's opening statement to highlight unique value

    Why:

    CURRENT
    Physical symbolic optimization ( $\Phi$-SO ) - A symbolic optimization package built for physics.
    COPY-PASTE FIX
    PhySO ($\Phi$-SO) is a physics-informed symbolic regression package that leverages a hybrid optimization approach, combining deep reinforcement learning with dimensional analysis to infer analytical physical laws from data.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    deep-learning, equation-discovery, machine-learning, physics, python, reinforcement-learning, symbolic-regression
    COPY-PASTE FIX
    deep-learning, equation-discovery, machine-learning, physics, python, reinforcement-learning, symbolic-regression, physics-informed-ai, dimensional-analysis, genetic-programming
  • lowcomparison#3
    Add a 'Why PhySO?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why PhySO?
PhySO differentiates itself from other symbolic regression libraries by integrating physics-informed constraints, such as dimensional analysis and units-awareness, directly into its deep reinforcement learning and genetic programming hybrid optimization framework. This allows it to efficiently discover interpretable physical laws, unlike generic symbolic regression tools or purely data-driven AI methods.

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
gplearn
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. gplearn · recommended 2×
  2. AI Feynman · recommended 2×
  3. Deep Symbolic Regression (DSR) · recommended 2×
  4. Eureqa · recommended 2×
  5. SymPy · recommended 2×
  • CATEGORY QUERY
    How can I automatically discover underlying physical equations from experimental data using AI?
    you: not recommended
    AI recommended (in order):
    1. PySINDy
    2. gplearn
    3. AI Feynman
    4. Deep Symbolic Regression (DSR)
    5. Eureqa
    6. SymPy

    AI recommended 6 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 physical laws with deep learning?
    you: not recommended
    AI recommended (in order):
    1. PySR
    2. gplearn
    3. Deep Symbolic Regression (DSR)
    4. SymPy
    5. SciPy
    6. NumPy
    7. Eureqa
    8. AI Feynman

    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?

Embed your GEO score

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MARKDOWN (README)
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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