Machine Learning (ML) is a subset of Artificial Intelligence (AI) that is the study of computer algorithms that improve automatically through experience (data). Deep Learning (DL) is a subset of ML that uses multi-layered neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others. Scientific Machine Learning (SciML), more specifically, consists of computational technologies that can be trained with scientific data to augment or automate human skills. ML has been very successful in areas such as computer vision, natural language processing, etc. But its application in scientific computing is relatively new, especially in Nuclear Engineering (NE). This workshop aims at augmenting the applications of AI/ML in scientific computing in NE, and promoting ML-based transformative solutions across various DOE missions.

This workshop includes presentations from five speakers. The topics are listed below:

1: Introduction, Uncertainty Quantification and Scientific Machine Learning, Dr. Xu Wu, Assistant Professor, North Carolina State University

2: NeuroEvolution Optimization with Reinforcement Learning, Dr. Majdi Radaideh, Research Scientist, Massachusetts Institute of Technology

3: A Machine Learning Approach for Scale Bridging in System-level Thermal-hydraulic Simulation, Dr. Han Bao, Computational Scientist, Idaho National Laboratory

4: Machine Learning Augmented Cross Section Evaluation, Dr. Massimiliano Fratoni, Xenel Distinguished Professor, University of California, Berkeley

5: Physics-Informed Machine Learning, Dr. Yang Liu, Nuclear Engineer, Argonne National Laboratory

Session Recording

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Resources

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Attachment — MC2021_SciML_Workshop_Xu_Wu

Attachment — MC2021_SciML_Workshop_Majdi_Radaideh

Attachment — MC2021_SciML_Workshop_Han_Bao

Attachment — MC2021_SciML_Workshop_Massimiliano_Fratoni

Attachment — MC2021_SciML_Workshop_Yang_Liu

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