  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.
