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Science-based simulation tools such as Finite Element (FE) models areroutinely used in scientific and engineering applications. While their successis strongly dependent on our understanding of underlying governing physicallaws, they suffer inherent limitations including trade-off betweenfidelity/accuracy and speed. The recent rise of Machine Learning (ML) proposesa theory-agnostic paradigm. In complex multi-physics problems, however,creating large enough datasets for successful training of ML models has provento be challenging. One promising strategy to bridge the divide between theseapproaches and take advantage of their respective strengths is Theory-GuidedMachine Learning (TGML) which aims to integrate physical laws into MLalgorithms. In this paper, three case studies on thermal management duringprocessing of advanced composites are presented and studied using FE, ML andTGML. A structured approach to incrementally adding increasingly complexphysics to training of TGML model is presented. The benefits of TGML over MLmodels are seen in more accurate predictions, particularly outside the trainingregion, and ability to train with small datasets. One benefit of TGML over FEis significant speed improvement to potentially develop real-time feedbacksystems. A recent successful implementation of a TGML model to assessproducibility of aerospace composite parts is presented.

Theory-Guided Machine Learning for Process Simulation of Advanced Composites