Physics-Guided AI for Large-Scale Spatiotemporal Data

There is a great interest in scientific communities for harnessing the power of AI in applications ranging from climate science to quantum chemistry. The common theme in many of these applications is that the data are spatiotemporal with governing physics. Unfortunately, today's ML approaches are mostly purely data-driven, i.e., they solely rely on (labeled) data for learning statistical patterns. Collecting labeled data can be quite expensive in real-world applications. Moreover, the resulting black-box AI models are difficult to interpret for domain scientists.