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Benchmarks: missing
Time to repro: a few days
1 risk flag

Results & Benchmarks

Freshness tier: cold
Direct + Inferred Evidence

No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.

APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs presents a autoregressive model approach for computer science.

Implementation Evidence Summary

Confidence: medium

thunil/Physics-Based-Deep-Learning is the closest maintained adjacent implementation (Matches contextual method/domain keyword: algorithm). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 1894 GitHub stars.

Reproduction Risks

  • Adjacent implementations are not paper-verified
  • Recommended repository is adjacent and not paper-verified.

Hardware Notes

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Evidence disclosure

Evidence graph: 3 refs, 3 links.

Utility signals: depth 65/100, grounding 75/100, status medium.

Implementation Comparison

Top 1 paths

Compare maintenance quality, reproducibility coverage, and evidence confidence before choosing a reproduction baseline.

tum-pbs/apebench
alternative
Maintenance: Active
Confidence: Low
Reproducibility: Strong

Strong overlap with paper title keywords · Community adoption signal (101 stars)

Stars
101
Last push
Jun 1, 2026 (19d ago)
CIReleasesDependencies

Risk flags

  • No Docker setup
  • Low confidence match

Implementation Status

No verified maintained repo

There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.

  • No maintained paper-verified implementation was found; start with the closest related repositories below.
  • Compare repo methods against the paper equations/algorithm before trusting metrics.
  • Create a minimal baseline implementation from the paper and use adjacent repos as references.
Time to first repro: a few days

Reproduction readiness

No Repo
Time to first repro: days
Last checked: Jun 18, 2026

Hardware requirements

  • Expect multi-day setup/compute for meaningful reproduction based on current guidance.

No verified implementation available

  • · No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.

Closest related implementations

These are not paper-verified. Use them as reference points when no direct implementation is available.

Additional implementations

No additional verified repositories beyond the primary recommendation.

These repositories had low-confidence matching signals and are hidden by default.

Hugging Face artifacts

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Research context

2

Citations

0

References

Tasks

Computer science, Artificial neural network, Benchmark (surveying), Noise (video), Context (archaeology), Signal processing, Time series, System identification

Methods

Autoregressive model, Algorithm, Mathematical optimization, Nonlinear autoregressive exogenous model

Domains

Artificial intelligence, Mathematics

Evaluation & Human Feedback Data

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Computer science Artificial neural network Benchmark (surveying) Noise (video) Context (archaeology) Signal processing

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