Driven by Data or Derived through Physics? Hybrid Physics Guided Machine Learning Approach

Abstract

A multitude of physical systems applications including design, control, diagnosis, prognostics, and host of other problems are predicated on the assumption of model availability. There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML). Modelbased methods assume the availability of an accurate system model while data-driven methods are based on machine learning. Purely data-driven ML methods ignore any knowledge about the physical/abstract systems. Additionally, ML approaches require a large amount of labeled training data that is typically unavailable. MB approaches require excellent physics models and good specification of parameters values. When building models of complex systems, we are often limited by the unavailability of the parameters of the system components due to incomplete technical specifications, hidden physical interactions or interactions that are too complex to model from first principles. Hence, we often make simplifying assumptions (e.g., linear approximations) and construct coarse models that imperfectly describe the behavior of the real system. A prudent approach is to use hybrid methods that use the physics of system and prior knowledge about the domain to guide construction of machine learning techniques such as Deep Neural Networks (DNNs). The principal goal of this talk is to discuss challenges related to the development of hybrid methods that combine Multi-physics equation-based models with data-driven machine learning models (such as DNNs) to enable predictive modeling of complex systems in the presence of imperfect models and sparse and noisy data. I will discuss connections to larger problems in the associated area and present specific results related to the development of novel hybrid methods.