Miles Cranmer, Shirley Ho, Yan-Fei Jiang, François Rozet, Bruno Régaldo-Saint Blancard, Rudy Morel, Michael Mccabe, Ruben Ohana, Liam Parker, Blakesley Burkhart, Jeff Shen, Keiya Hirashima, and 16 more
We lifted 13 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lanl/nubhlight | canonical | 0 of 9 |
| PolymathicAI/the_well | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| build_1d_power_spectrum | Not yet run | PolymathicAI/the_well/the_well/benchmark/metrics/plottable_data.py code served (permissive licence) · get_code("cf1e8ae5dfb35212") |
| get_data | Not yet run | lanl/nubhlight/script/analysis/accumulate_skynet_mass_fractions.py pointer only (licence: NOASSERTION) · get_code("81106a3d566379ad") |
| get_data | Not yet run | lanl/nubhlight/script/analysis/accumulate_skynet_output.py pointer only (licence: NOASSERTION) · get_code("e8dede4e621a64e5") |
| get_env_name | Not yet run | lanl/nubhlight/script/config.py pointer only (licence: NOASSERTION) · get_code("d40053a9a7e821e4") |
| get_files | Not yet run | lanl/nubhlight/script/util.py pointer only (licence: NOASSERTION) · get_code("7990041b657e98a1") |
| get_header | Not yet run | lanl/nubhlight/script/analysis/accumulate_skynet_mass_fractions.py pointer only (licence: NOASSERTION) · get_code("da272b91191facb4") |
| get_header | Not yet run | lanl/nubhlight/script/analysis/accumulate_skynet_output.py pointer only (licence: NOASSERTION) · get_code("bd294fc3c02bee96") |
| get_metadata | Not yet run | lanl/nubhlight/script/analysis/accumulate_skynet_mass_fractions.py pointer only (licence: NOASSERTION) · get_code("c6c8065f319c5d7f") |
| hdf5_to_xarray | Not yet run | PolymathicAI/the_well/the_well/utils/export.py code served (permissive licence) · get_code("52905a530370ca29") |
| param_norm | Not yet run | PolymathicAI/the_well/the_well/benchmark/trainer/training.py code served (permissive licence) · get_code("c43574b335efa848") |
| parm_is_active | Not yet run | lanl/nubhlight/script/util.py pointer only (licence: NOASSERTION) · get_code("c812dff903ab1f9f") |
| sanitize_path | Not yet run | lanl/nubhlight/script/util.py pointer only (licence: NOASSERTION) · get_code("0e652a3519220159") |
| vector_dtw | Not yet run | PolymathicAI/the_well/the_well/benchmark/metrics/temporal.py code served (permissive licence) · get_code("eb87dbb5e169f5be") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2412.00568")
get_code_for_paper("2412.00568")
have("2412.00568")
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