RRepoGEO

REPOGEO REPORT · LITE

deepmodeling/jax-fem

Default branch main · commit 89b702b6 · scanned 6/15/2026, 5:47:54 AM

GitHub: 693 stars · 122 forks

AI VISIBILITY SCORE
67 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 deepmodeling/jax-fem, 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 emphasize inverse design and AD for FEM

    Why:

    CURRENT
    JAX-FEM is a differentiable finite element package based on JAX.
    COPY-PASTE FIX
    JAX-FEM is a high-performance, differentiable finite element package built on JAX, specifically designed for scientific machine learning, inverse design, and optimization problems that leverage automatic differentiation for FEM simulations.
  • mediumtopics#2
    Add more specific topics related to inverse design and scientific machine learning

    Why:

    CURRENT
    differentiable-programming, finite-element-methods, jax, topology-optimization
    COPY-PASTE FIX
    differentiable-programming, finite-element-methods, jax, topology-optimization, inverse-design, scientific-machine-learning, physics-informed-machine-learning, computational-mechanics
  • lowabout#3
    Refine the repository description to highlight inverse design capabilities

    Why:

    CURRENT
    Differentiable Finite Element Method with JAX
    COPY-PASTE FIX
    Differentiable Finite Element Method (FEM) with JAX for scientific machine learning, inverse design, and optimization.

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
1 / 2
50% of queries surface deepmodeling/jax-fem
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
FEniCSx
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FEniCSx · recommended 1×
  2. DeepXDE · recommended 1×
  3. PyTorch-FEM · recommended 1×
  4. JAX · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How to perform differentiable finite element analysis for topology optimization using JAX?
    you: #1
    AI recommended (in order):
    1. JAX-FEM ← you
    2. FEniCSx
    3. DeepXDE
    4. PyTorch-FEM
    Show full AI answer
  • CATEGORY QUERY
    What tools enable automatic differentiation for FEM simulations in inverse design problems?
    you: not recommended
    AI recommended (in order):
    1. JAX
    2. PyTorch
    3. TensorFlow
    4. FEniCS Project
    5. dolfin-adjoint
    6. OpenAD
    7. ADOL-C
    8. TAPENADE
    9. Zygote.jl

    AI recommended 9 alternatives but never named deepmodeling/jax-fem. 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 deepmodeling/jax-fem?
    pass
    AI did not name deepmodeling/jax-fem — 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?

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

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

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  • Brand-free category queries5 vs 2 in Lite
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