RRepoGEO

REPOGEO REPORT · LITE

tum-pbs/PhiFlow

Default branch master · commit 31a200a5 · scanned 5/19/2026, 11:02:08 PM

GitHub: 1,871 stars · 226 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)

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

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 tum-pbs/PhiFlow, 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 the README's opening sentence to be more specific

    Why:

    CURRENT
    Φ<sub>Flow</sub> is an open-source simulation toolkit built for optimization and machine learning applications.
    COPY-PASTE FIX
    Φ<sub>Flow</sub> is an open-source **differentiable PDE solving framework** for machine learning, specializing in **fluid simulations** and other physics-based problems. It is built for optimization and machine learning applications, integrating with NumPy, PyTorch, Jax, or TensorFlow.
  • highhomepage#2
    Add the project homepage to the repository metadata

    Why:

    COPY-PASTE FIX
    https://tum-pbs.github.io/PhiFlow/
  • mediumreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

PhiFlow differentiates itself from other differentiable physics frameworks like DiffTaichi, JAX-MD, Brax, and Diffrax by offering a unified, high-level API for differentiable PDE solving, with a strong focus on fluid simulations and seamless integration across multiple deep learning backends (NumPy, PyTorch, JAX, TensorFlow).

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 tum-pbs/PhiFlow
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DiffTaichi
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DiffTaichi · recommended 1×
  2. google/jax · recommended 1×
  3. google/jax-md · recommended 1×
  4. google/brax · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    Seeking a framework for end-to-end differentiable physics simulations in machine learning applications.
    you: not recommended
    AI recommended (in order):
    1. DiffTaichi
    2. JAX (google/jax)
    3. JAX-MD (google/jax-md)
    4. Brax (google/brax)
    5. PyTorch (pytorch/pytorch)
    6. PyTorch3D (facebookresearch/pytorch3d)
    7. TensorFlow (tensorflow/tensorflow)
    8. TensorFlow Graphics (tensorflow/graphics)
    9. Isaac Gym
    10. DeepMind's Physics Engine

    AI recommended 10 alternatives but never named tum-pbs/PhiFlow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I integrate differentiable PDE solvers for fluid simulations into my ML models?
    you: not recommended
    AI recommended (in order):
    1. JAX
    2. JAX-MD
    3. Diffrax
    4. Equinox
    5. PyTorch
    6. torch_ode
    7. torchdyn
    8. TensorFlow
    9. TensorFlow Probability
    10. Modulus
    11. OpenFOAM

    AI recommended 11 alternatives but never named tum-pbs/PhiFlow. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 tum-pbs/PhiFlow?
    pass
    AI named tum-pbs/PhiFlow explicitly

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

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

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

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tum-pbs/PhiFlow — 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