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MULTIFID-TH β€” multifidelity surrogates for thermal-hydraulics. High-fidelity MOOSE simulations inform low-fidelity ML surrogates through a single generic training core.
MULTIFID-TH
MULTIFID-TH

Overview

  • Architecture

Guides

  • User Guide
    • Getting Started
    • Running MOOSE Simulations
    • Alpha-D Surrogate: Axial-Profile MLP Tutorial
    • Hyperparameter Optimization
    • Case Distribution Analysis
  • Cases
    • MOOSE Grid Case
    • Alpha-D Case
    • Case Pressure-Drop Surrogate

Reference

  • Developer Guide
    • ETL Pipeline
    • MOOSEDataset API
    • FNO Training and Evaluation
    • Alpha-D Surrogate β†’ MOOSE PINSFV Coupling: Physics Reference
    • Code style and pre-commit
    • Building the documentation
  • API Reference
    • training
    • cases
    • feature_selection
    • dataset
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Developer GuideΒΆ

Internals of the ETL pipeline, the public dataset API, and the training/evaluation loop. Read these alongside the Architecture page when you need to extend the trainer, add a new model, or audit how a particular surrogate is wired together.

Pipeline internals

  • ETL Pipeline
  • MOOSEDataset API
  • FNO Training and Evaluation

Physics & coupling

  • Alpha-D Surrogate β†’ MOOSE PINSFV Coupling: Physics Reference

Contributor reference

  • Code style and pre-commit
  • Building the documentation
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ETL Pipeline
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Case Pressure-Drop Surrogate
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