Theory-Guided Machine Learning for Process Simulation of Advanced Composites

Understand

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.