Poster
in
Workshop: AI for Science: Mind the Gaps

On the feasibility of small-data learning in simulation-driven engineering tasks with known mechanisms and effective data representations

Haosu Zhou · Hamid Attar


Abstract:

The application of machine learning (ML) in scientific tasks is increasing, especially ML with structured representations in simulation-driven engineering tasks. While previous studies stuck to large-data learning, recent studies are investigating small-data learning and effective, case-specific representations, which is significant for industrial practice. This article provides a theoretical discussion for the feasibility of small-data learning with structured representations, which is then verified through the surrogate modelling of hot stamping simulations. Future directions are also discussed.