| --- |
| license: mit |
| --- |
| |
| # DRNO sanity outputs |
|
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| This repository mirrors `outputs/sanity/`, the full sanity experiment output directory for Discrete-Residual Neural Operators (DRNO). The artifacts were generated by the code at <https://github.com/soundai2016/DRNO> with `configs/sanity.yaml`. |
|
|
| ## Download |
|
|
| ```bash |
| pip install --upgrade huggingface_hub |
| hf download soundai2016/drno --local-dir outputs/sanity |
| ``` |
|
|
| ## Contents |
|
|
| ```text |
| outputs/sanity/ |
| data/ generated PDE datasets: .npz arrays + .json metadata |
| checkpoints/ PyTorch checkpoints for each task/model/seed/lambda/selector |
| histories/ per-epoch training logs |
| results/ CSV metrics, Pareto grids, uncertainty, runtime and audit ledgers |
| figures/ analysis figures from the plot stage |
| tables/ JSON tables derived from results/*.csv |
| pdebench_hdf5/ PDEBench-style HDF5 exports, manifest and audit summary |
| publication/ manuscript-ready figures, tables and source data |
| resolved_config.json |
| run_environment.json |
| publication_package.json |
| publication_figures_run.json |
| pdebench_manifest_summary.json |
| ``` |
|
|
| ## Scope |
|
|
| The sanity run covers four PDE tasks: `darcy`, `burgers`, `schrodinger`, and `ns2d_kolmogorov`; train/val/test/OOD/mesh-transfer splits; seeds `0..4`; FNO, PINO-style residual, DRNO solver-consistent residual, MC-dropout FNO, DeepONet (Darcy/Burgers) and weak-form residual (Darcy/Burgers) baselines. |
|
|
| ## Naming convention |
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| ```text |
| data/{task}_{split}.npz |
| histories/{task}_{model}_seed{seed}_lam{lambda}_{tag}.csv |
| checkpoints/{task}_{model}_seed{seed}_lam{lambda}_{tag}_{selector}.pt |
| ``` |
|
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| Common `tag` values are `main` and `sweep`. Common checkpoint selectors are `best_rel`, `best_res`, `best_qoi`, `best_pareto`, and `final`. Decimal residual weights are filename-safe, e.g. `0.2 -> 0p2`. |
|
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| ## Reproduce or extend |
|
|
| ```bash |
| git clone https://github.com/soundai2016/DRNO |
| cd DRNO |
| python -m pip install -e . |
| |
| # Recreate the full suite from scratch. |
| bash scripts/run_sanity.sh |
| |
| # Or reuse downloaded artifacts under outputs/sanity/ and rerun a stage. |
| python -m drno.run_all --config configs/sanity.yaml --stage audit |
| python -m drno.run_all --config configs/sanity.yaml --stage plot --force |
| ``` |
|
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| The full pipeline is: |
|
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| ```text |
| generate -> train -> sweep -> eval -> pareto -> uncertainty -> benchmark -> runtime -> pdebench -> plot -> audit |
| ``` |
|
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| ## Notes |
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| - `resolved_config.json` records the exact normalized configuration used for the run. |
| - `run_environment.json` records the software and hardware environment. |
| - `.pt` checkpoint files are PyTorch serialized artifacts; load them only in a trusted Python environment. |
| - CSV/JSON/HDF5 files are intended for analysis and reproduction; figures and `publication/` are derived display artifacts. |
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|
