On the Use of Physics in Machine Learning for Inspection and Control of Complex Processes

Machine learning operators, such as neural networks, are universal function approximators—albeit, in practice, their generalization ability depends on the quality of the training data and the algorithm designer's wisdom in choosing a particular operator form, i.e. how well it matches the function at hand. Scientific machine learning is a class of methods that constrain the neural network operator by forcing its output to match time-series data from a partially known dynamical model, e.g. an ordinary or partial vector differential equation. In this talk, we make the case for regularizing optical image measurements using this approach. Applications are expected to be in processes with high-complexity constitutive relationships, such as pharmaceutical and cell manufacturing, plant biology, and ecology.