MULTIFID-TH

MULTIFID-TH is a PhysicsNeMo-based pipeline that turns MOOSE thermal-hydraulics outputs (Exodus .e files plus CSV line probes) into ML-ready Zarr datasets, then trains and evaluates a family of surrogate models against them through a single generic training core.

        flowchart LR
    A["MOOSE outputs<br/>(.e + CSV probes)"]
    B["ETL pipeline<br/>(read · transform · validate)"]
    C["Processed dataset<br/>(*.zarr)"]
    D["MOOSEDataset<br/>(graph · point_cloud · grid)"]
    A --> B --> C --> D

What it does

  • Reads simulation outputs from Exodus .e files and CSV line-probe files.

  • Normalizes fields and constructs graph and regular-grid representations.

  • Writes one compressed .zarr store per simulation run.

  • Exposes a PyTorch Dataset for graph, point-cloud, and grid training, plus per-case tabular Zarr for pointwise / profile surrogates.

Architecture at a glance

        flowchart LR
    subgraph ETL["Two ETL pipelines"]
        E1["cases/moose_grid<br/>(run_etl.py)"]
        E2["cases/alpha_d<br/>(run_etl.py)"]
    end
    subgraph DATA["Per-case Zarr"]
        Z1["{sim_name}.zarr<br/>mesh · fields · grid · probes"]
        Z2["{case}.zarr<br/>features · targets · metadata"]
    end
    subgraph TRAIN["One generic trainer"]
        TF["train.py / evaluate.py<br/>adapters: grid · graph · pointwise · profile"]
    end
    E1 --> Z1
    E2 --> Z2
    Z1 --> TF
    Z2 --> TF
    TF --> M["FNO · MeshGraphNet · AFNO · Pix2Pix<br/>MLP · Conv1DProfile"]

The whole story in one sentence: two ETL pipelines feed one trainer through four adapters. The deeper structure is on the Architecture page.

Where do I start?

First time

Container setup, bind mounts, troubleshooting, and log inspection.

Open the Getting Started guide.

Getting Started
Alpha-D surrogate

Reproduce the Darcy-resistance MLP end-to-end, ETL through evaluation.

Open the Alpha-D Surrogate tutorial.

Alpha-D Surrogate: Axial-Profile MLP Tutorial
New model

How adapters, datasets, and the runner fit together when adding a model.

Open the Architecture page.

Architecture
Coupling physics

Equations, averaging conventions, and the α_D → MOOSE Forchheimer mapping with the resolved CFD vs PINSFV verification.

Open the Physics Reference.

Alpha-D Surrogate → MOOSE PINSFV Coupling: Physics Reference

Quick start

git submodule update --init physicsnemo-curator physicsnemo
docker compose build etl-dev
docker compose run --rm etl-dev bash -lc \
  'cd src && python cases/moose_grid/run_etl.py'

git submodule update --init physicsnemo-curator physicsnemo
apptainer build multifid-th-cpu.sif docker/physicsnemo-cpu.def
apptainer exec --bind /path/to/project:/path/to/project \
  multifid-th-cpu.sif bash -lc 'cd /path/to/project/src && python cases/moose_grid/run_etl.py'

The default config is the lid-driven flow at src/cases/moose_grid/configs/etl.yaml, which writes output to data/processed/lid-driven/*.zarr.

Train an FNO

After ETL generates *.zarr stores, train with the generic framework using the FNO example config at src/cases/moose_grid/configs/train_fno.yaml:

docker compose run --rm etl bash -lc \
  'cd src && python train.py --config-path cases/moose_grid/configs --config-name train_fno'

Use etl-ngc if you prefer the NGC PhysicsNeMo base image; CLI flags override YAML values, for example training.epochs=50.

Train the Alpha-D MLP surrogate

The alpha-D workflow extracts Darcy-resistance coefficient profiles from a parametric study of flow contraction-expansion simulations and trains a PhysicsNeMo FullyConnected MLP:

# 1. Extract alpha_D profiles from CFD output
docker compose run --rm etl bash -lc \
  'cd src && python cases/alpha_d/run_etl.py'

# 2. PyCaret feature selection (writes selected_features.txt that
#    train_mlp consumes via data.input_columns_file)
docker compose run --rm etl bash -lc \
  'cd src && python cases/alpha_d/run_feature_selection_pycaret.py'

# 3. Train (HPO + retrain best, one command)
docker compose run --rm etl bash -lc \
  'cd src && python train.py --config-path cases/alpha_d/configs --config-name train_mlp'

# 3b. Or skip HPO and train directly
docker compose run --rm etl bash -lc \
  'cd src && python train.py --config-path cases/alpha_d/configs --config-name train_mlp hpo=null'

# 4. Evaluate
docker compose run --rm etl bash -lc \
  'cd src && python evaluate.py --config-path cases/alpha_d/configs --config-name train_mlp'

The Conv1D path (--config-name train_conv1d) hard-codes its input columns and does not need step 2.

The full walkthrough is in the Alpha-D Surrogate Tutorial.

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Reference