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PoreML_checkpoint

PyTorch
physics
computational-fluid-dynamics
porous-media
multiphase-flow
scientific-machine-learning
neural-operator
3d
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  1. README.md +88 -0
  2. checkpoints.csv +176 -0
  3. train/drainage/abupt_all/ckpts/abupt_drainage_all_20260904-051037/config.yaml +267 -0
  4. train/drainage/abupt_all/ckpts/abupt_drainage_all_20260904-051037/metrics.csv +11 -0
  5. train/drainage/abupt_all/ckpts/abupt_drainage_all_20260904-051037/run_meta.json +0 -0
  6. train/drainage/abupt_all/ckpts/abupt_drainage_all_20260904-051037/train_log.csv +0 -0
  7. train/drainage/abupt_gen/ckpts/abupt_drainage_gen_20260904-051157/config.yaml +267 -0
  8. train/drainage/abupt_gen/ckpts/abupt_drainage_gen_20260904-051157/metrics.csv +21 -0
  9. train/drainage/abupt_gen/ckpts/abupt_drainage_gen_20260904-051157/run_meta.json +2530 -0
  10. train/drainage/abupt_gen/ckpts/abupt_drainage_gen_20260904-051157/train_log.csv +0 -0
  11. train/drainage/fno_all/ckpts/fno_drainage_all_20260904-044637/config.yaml +261 -0
  12. train/drainage/fno_all/ckpts/fno_drainage_all_20260904-044637/metrics.csv +11 -0
  13. train/drainage/fno_all/ckpts/fno_drainage_all_20260904-044637/run_meta.json +0 -0
  14. train/drainage/fno_all/ckpts/fno_drainage_all_20260904-044637/train_log.csv +1151 -0
  15. train/drainage/fno_gen/ckpts/fno_drainage_gen_20260904-044637/config.yaml +261 -0
  16. train/drainage/fno_gen/ckpts/fno_drainage_gen_20260904-044637/metrics.csv +21 -0
  17. train/drainage/fno_gen/ckpts/fno_drainage_gen_20260904-044637/run_meta.json +2516 -0
  18. train/drainage/fno_gen/ckpts/fno_drainage_gen_20260904-044637/train_log.csv +1161 -0
  19. train/drainage/p3d_all/ckpts/p3d_drainage_all_20260904-044637/config.yaml +260 -0
  20. train/drainage/p3d_all/ckpts/p3d_drainage_all_20260904-044637/metrics.csv +11 -0
  21. train/drainage/p3d_all/ckpts/p3d_drainage_all_20260904-044637/run_meta.json +0 -0
  22. train/drainage/p3d_all/ckpts/p3d_drainage_all_20260904-044637/train_log.csv +1151 -0
  23. train/drainage/p3d_gen/ckpts/p3d_drainage_gen_20260904-044806/config.yaml +260 -0
  24. train/drainage/p3d_gen/ckpts/p3d_drainage_gen_20260904-044806/metrics.csv +21 -0
  25. train/drainage/p3d_gen/ckpts/p3d_drainage_gen_20260904-044806/run_meta.json +2514 -0
  26. train/drainage/p3d_gen/ckpts/p3d_drainage_gen_20260904-044806/train_log.csv +1161 -0
  27. train/drainage/transolver_all/ckpts/transolver_drainage_all_20260904-063839/config.yaml +265 -0
  28. train/drainage/transolver_all/ckpts/transolver_drainage_all_20260904-063839/metrics.csv +11 -0
  29. train/drainage/transolver_all/ckpts/transolver_drainage_all_20260904-063839/run_meta.json +0 -0
  30. train/drainage/transolver_all/ckpts/transolver_drainage_all_20260904-063839/train_log.csv +0 -0
  31. train/drainage/transolver_gen/ckpts/transolver_drainage_gen_20260904-081310/config.yaml +265 -0
  32. train/drainage/transolver_gen/ckpts/transolver_drainage_gen_20260904-081310/metrics.csv +21 -0
  33. train/drainage/transolver_gen/ckpts/transolver_drainage_gen_20260904-081310/run_meta.json +2526 -0
  34. train/drainage/transolver_gen/ckpts/transolver_drainage_gen_20260904-081310/train_log.csv +0 -0
  35. train/drainage/unet_all/ckpts/unet_drainage_all_20260904-045838/config.yaml +253 -0
  36. train/drainage/unet_all/ckpts/unet_drainage_all_20260904-045838/metrics.csv +11 -0
  37. train/drainage/unet_all/ckpts/unet_drainage_all_20260904-045838/run_meta.json +0 -0
  38. train/drainage/unet_all/ckpts/unet_drainage_all_20260904-045838/train_log.csv +1151 -0
  39. train/drainage/unet_gen/ckpts/unet_drainage_gen_20260904-051036/config.yaml +253 -0
  40. train/drainage/unet_gen/ckpts/unet_drainage_gen_20260904-051036/metrics.csv +21 -0
  41. train/drainage/unet_gen/ckpts/unet_drainage_gen_20260904-051036/run_meta.json +2498 -0
  42. train/drainage/unet_gen/ckpts/unet_drainage_gen_20260904-051036/train_log.csv +1161 -0
  43. train/gdl/abupt_all/ckpts/abupt_gdl_all_20260904-061547/config.yaml +267 -0
  44. train/gdl/abupt_all/ckpts/abupt_gdl_all_20260904-061547/metrics.csv +11 -0
  45. train/gdl/abupt_all/ckpts/abupt_gdl_all_20260904-061547/run_meta.json +0 -0
  46. train/gdl/abupt_all/ckpts/abupt_gdl_all_20260904-061547/train_log.csv +0 -0
  47. train/gdl/abupt_gen/ckpts/abupt_gdl_gen_20260904-061547/config.yaml +267 -0
  48. train/gdl/abupt_gen/ckpts/abupt_gdl_gen_20260904-061547/metrics.csv +21 -0
  49. train/gdl/abupt_gen/ckpts/abupt_gdl_gen_20260904-061547/train_log.csv +0 -0
  50. train/gdl/fno_all/ckpts/fno_gdl_all_20260904-051037/config.yaml +261 -0
README.md CHANGED
@@ -1,3 +1,91 @@
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  ---
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  license: mit
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: mit
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+ pretty_name: PoreML V1 — reference model checkpoints
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+ library_name: pytorch
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+ datasets:
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+ - PoreML/PoreML_data
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+ tags:
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+ - physics
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+ - computational-fluid-dynamics
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+ - porous-media
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+ - multiphase-flow
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+ - scientific-machine-learning
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+ - neural-operator
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+ - 3d
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  ---
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+
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+ # PoreML V1 — reference model checkpoints
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+
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+ The trained weights behind the PoreML benchmark: five reference models — `unet3d`, `fno3d`,
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+ `p3d`, `transolver`, `abupt` — on the next-frame forecasting task of
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+ [`PoreML/PoreML_data`](https://huggingface.co/datasets/PoreML/PoreML_data), in two phases.
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+
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+ | Phase | Folder | Runs | Report this checkpoint |
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+ |---|---|---:|---|
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+ | base training, 20 effective epochs | `train/` | 35 | `ckpts/best.pt` — best one-step validation `mae@phi` |
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+ | push-forward fine-tune of each base run | `train_push/` | 35 | `ckpts/best_rollout.pt` — best 64-step rollout `mae@phi` |
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+
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+ 35 = 5 models × 7 tasks: the `gen` (generated media) and `all` (generated ∪ micro-CT) splits of
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+ drainage, GDL and trapping, plus underfill's single `all` split.
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+
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+ ## Download
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+
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+ With the PoreML code repository checked out and installed (`uv sync`):
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+
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+ ```bash
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+ uv run poreml checkpoints --dry-run # what is there and how big it is (7 GB)
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+ uv run poreml checkpoints --phase train_push --which best_rollout # the weights the paper reports
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+ uv run poreml checkpoints --campaign drainage --model unet --kind gen
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+ uv run poreml checkpoints # everything
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+ ```
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+
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+ Filters are `--phase`, `--campaign`, `--model`, `--kind` and `--which`; they intersect and each
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+ is repeatable. Files land under `case/`, which is where the push configs' `train.init_from`, the
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+ transfer studies (`case/shift`, `case/scale`) and `util/inference` look a finished run up —
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+ nothing has to be moved. Rerunning resumes. Without the package:
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+
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+ ```bash
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+ hf download PoreML/PoreML_checkpoint --local-dir case --exclude README.md checkpoints.csv
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+ ```
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+
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+ ## Layout
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+
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+ The repository root is the benchmark's `case/` folder:
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+
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+ ```
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+ <phase>/<campaign>/<model>_<kind>/ckpts/<run>/
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+ config.yaml the full config the run was trained with; a checkpoint is always scored with it
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+ run_meta.json status, resolved data and split (with sha256), model size, provenance, progress
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+ metrics.csv one row per epoch: losses, learning rate, every validation metric
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+ train_log.csv the training loss every 100 steps
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+ ckpts/best.pt best one-step validation metric
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+ ckpts/last.pt the final epoch
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+ ckpts/best_rollout.pt best rollout metric (push-forward runs)
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+ checkpoints.csv every weight file: phase, campaign, model, kind, path, epoch, bytes, sha256
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+ ```
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+
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+ A `.pt` is a `torch.save`d dict: `model` (the state dict), `config` (the model's name, params
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+ and precision), `epoch`, `metrics`. `run_meta.json` of a push-forward run records the path and
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+ sha256 of the base `best.pt` it started from; it matches `checkpoints.csv`.
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+
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+ Not included: optimiser state, the per-epoch rollout candidates, and stored rollout frames —
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+ `poreml inference`, `poreml metric` and `poreml render` reproduce those from the weights.
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+
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+ ## Use
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+
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+ ```bash
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+ RUN=case/train_push/drainage/unet_gen/ckpts/<run>
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+ uv run poreml rollout -c $RUN/config.yaml --ckpt $RUN/ckpts/best_rollout.pt # one scored 64-step rollout per validation run
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+ uv run python case/shift_push/submit.py --dry-run # the transfer studies see the runs as finished
80
+ ```
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+
82
+ The runs were trained on single NVIDIA H200 GPUs with PyTorch 2.11 (CUDA 12.8), `tf32`
83
+ everywhere except AB-UPT's `bf16`. Paths inside the text files are relative to the code
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+ repository's root; the node's hostname has been removed from `run_meta.json`, and nothing else
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+ was changed.
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+
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+ ## Licence
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+
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+ MIT for the weights of `unet3d`, `fno3d`, `p3d` and `transolver`. The `abupt` weights are
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+ derived from the AB-UPT architecture and follow its upstream Emmi AI Non-Production License:
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+ research and evaluation use only.
checkpoints.csv ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ phase,campaign,model,kind,path,epoch,bytes,sha256
2
+ train,drainage,abupt,all,train/drainage/abupt_all/ckpts/abupt_drainage_all_20260904-051037/ckpts/best.pt,9,42237479,bc5365bfa80c090708a639d74635f1adca887679da7b97f93d989e590c50aa12
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+ train,drainage,abupt,all,train/drainage/abupt_all/ckpts/abupt_drainage_all_20260904-051037/ckpts/last.pt,9,42237479,2ed4621ae8ca8c98287315dd24c65b040835e7b242079fd2eba7156eef797a16
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+ train,drainage,abupt,gen,train/drainage/abupt_gen/ckpts/abupt_drainage_gen_20260904-051157/ckpts/best.pt,19,42237479,2fbc09fed9fb5aa9e2d80d9839a8bfe7a4fdbc8e9afb16ab088b280a39507889
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+ train,drainage,fno,all,train/drainage/fno_all/ckpts/fno_drainage_all_20260904-044637/ckpts/best.pt,7,77669867,d3c17cbb5973c61a5685310440e5f74fba0a33c6ee545947570da2ad193d6e49
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+ train,drainage,fno,gen,train/drainage/fno_gen/ckpts/fno_drainage_gen_20260904-044637/ckpts/best.pt,3,77669867,190cd683527b148f7dd4d309f5f6f0745b01b82140d8a3e369daf235bc342aa2
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+ train,drainage,p3d,all,train/drainage/p3d_all/ckpts/p3d_drainage_all_20260904-044637/ckpts/best.pt,8,43376419,5f47e054e358bcc6d2e2da2a32086e5a8d9e640eb3c2a37dfb60881d40f51c6c
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+ train,drainage,transolver,all,train/drainage/transolver_all/ckpts/transolver_drainage_all_20260904-063839/ckpts/best.pt,9,7013696,912f1ca937b2928e5e0ccfac0d8bc18b1f63047b10d583db0fc73ca789625980
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The diff for this file is too large to render. See raw diff
 
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