Physics-informed Machine Learning for Control – DNN in a Dynamic Feedback Loop

This Case Study describes an approach to combining physical principles with Machine Learning (ML) for modeling and control of complex systems. Our approach was developed as part of a DARPA-funded research project. It was applied to oil reservoir management. While this Case Study provides an overview, technical details may be found in a separate publication [1].

Reactor-scale Modeling of Silicon Epitaxy

This case study of epitaxial deposition of silicon film on a silicon substrate in a horizontal hot-wall reactor reproduces an earlier modeling study by Habuka et al. [1]. The steady-state finite element (FEM) model incorporates fluid flow, heat transfer, dilute...