Science-based simulation tools such as Finite Element (FE) models are routinely used in scientific and engineering applications. While their success is strongly dependent on our understanding of underlying governing physical laws, they suffer inherent limitations including trade-off between fidelity/accuracy and speed. The recent rise of Machine Learning (ML) proposes a theory-agnostic paradigm. In complex multi-physics problems, however, creating large enough datasets for successful training of ML models has proven to be challenging. One promising strategy to bridge the divide between these approaches and take advantage of their respective strengths is Theory-Guided Machine Learning (TGML) which aims to integrate physical laws into ML algorithms. In this paper, three case studies on thermal management during processing of advanced composites are presented and studied using FE, ML and TGML. A structured approach to incrementally adding increasingly complex physics to training of TGML model is presented. The benefits of TGML over ML models are seen in more accurate predictions, particularly outside the training region, and ability to train with small datasets. One benefit of TGML over FE is significant speed improvement to potentially develop real-time feedback systems. A recent successful implementation of a TGML model to assess producibility of aerospace composite parts is presented.
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
Predicting Stress and Damage in Carbon Fiber-Reinforced Composites Deformation Process using Composite U-Net Surrogate Model
Department of Chemical and Biomolecular Engineering, University of Notre Dame（化学与生物分子工程系，圣母大学）；Department of Aerospace Engineering, University of Colorado Boulder（航空航天工程系，科罗拉多大学波德分校）；Department of Aerospace and Mechanical Engineering, University of Notre Dame（航空航天与机械工程系，圣母大学）；Air Force Research Laboratory, Wright-Patterson Air Force Base（空军研究实验室，威格斯-帕特森空军基地）
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
Federal Center for Technological Education (CEFET/RJ)（联邦里约热内卢技术教育中心）；Serviço Federal de Processamento de Dados (SERPRO)（联邦数据处理服务公司）；Instituto Politécnico de Bragança (IPB)（布拉干萨理工学院）
Defining Energy Indicators for Impact Identification on Aerospace Composites: A Structured Feature Selection Approach Guided by Domain Knowledge
Department of Mechanics of Solids, Surfaces and Systems, Dynamics Based Maintenance (DBM) Group, University of Twente（固体力学、表面与系统部门，基于动态的维护（DBM）小组，特文特大学）；Department of Aerospace Vehicles Integrity and Life Cycle Support (AVIL), Royal Netherlands Aerospace Centre（航空器完整性与生命周期支持（AVIL）部门，荷兰皇家航空研究中心）
Machine learning-guided synthesis of advanced inorganic materials
School of Materials Science and Engineering, Nanyang Technological University（南洋理工大学材料科学与工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing)（中国石油大学（北京）重质油国家重点实验室）；Centre for Micro-/Nano-electronics (NOVITAS), School of Electrical & Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院微/纳米电子中心（NOVITAS））；CINTRA CNRS/NTU/THALES, UMI 3288（法国国家科学研究中心-南洋理工大学-泰雷兹联合实验室（CINTRA UMI 3288））；Chemistry and Materials Centre, Nanyang Environment and Water Research Institute（南洋环境与水研究所化学与材料中心）
Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning
National ASIC System Engineering Technology Research Center, Southeast University, Nanjing, China（南京大学国家ASIC系统工程技术研发中心）；National Center of Technology Innovation for EDA, Nanjing, China（南京EDA技术创新国家中心）；University of California, Los Angeles, United States（加州大学洛杉矶分校）；Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China（宁波数字孪生研究所）；The University of Sheffield, Sheffield, United Kingdom（谢菲尔德大学）
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Department of Physics and Astronomy（物理与天文学系）；Dartmouth College（达特茅斯学院）；Brigham Young University（Brigham Young大学）
