Enabling scientific machine learning in MOOSE using Libtorch
A neural-network-based machine learning interface has been developed for the Multiphysics Object-Oriented Simulation Environment (MOOSE). The interface relies on Libtorch, the C++ front-end of PyTorch, and enables an online interaction between modern machine learning algorithms and all the existing simulation, modeling, and analysis processes available in MOOSE. New capabilities in MOOSE include the native generation and training of artificial neural networks together with options to load pretrained neural networks in TorchScript format. Furthermore, the MOOSE stochastic tools module (MOOSE-STM) has been enhanced with neural network-based surrogate and reduced-order model generation options for efficient stochastic analyses. Lastly, a reinforcement learning capability has been added to MOOSE-STM for the interactive control and optimization of complex multiphysics problems.
- Research Organization:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- AC07-05ID14517
- OSTI ID:
- 2331444
- Report Number(s):
- INL/JOU--22-69491-Revision-0
- Journal Information:
- SoftwareX, Journal Name: SoftwareX Journal Issue: - Vol. 23; ISSN 2352-7110
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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