APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs
Results and benchmarks
APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs presents a autoregressive model approach for computer science.
Benchmark evidence is limited
Evidence graph: 3 refs, 3 links.
Utility signals: depth 65/100, grounding 75/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
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
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: 1907 GitHub stars.
- Adjacent implementations are not paper-verified
- Recommended repository is adjacent and not paper-verified.
Reproduction readiness
No repo
No verified implementation available
- No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Repositories and ecosystem
Closest related implementations
These are not paper-verified. Use them as reference points when no direct implementation is available.
- thunil/Physics-Based-Deep-Learning Adjacent · Confidence: Medium · 1,907 stars
Matches contextual method/domain keyword: algorithm
No additional verified repositories beyond the primary recommendation.
These repositories had low-confidence matching signals and are hidden by default.
- tum-pbs/apebench
Confidence: Low · 108 stars
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
Tip: start with models, then check datasets and spaces if you need evaluation data or demos.
Research context
7
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
Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.
Open in HFEPXJump to Paper2Code search queries derived from this paper's research context.