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Bio: Aditi Krishnapriyan is the 2020 Alvarez Fellow in Computing Sciences at Lawrence Berkeley National Laboratory and UC Berkeley. Previously, she received a PhD at Stanford University, supported by the Department of Energy Computational Science Graduate Fellowship. During her PhD, she also spent time working on machine learning research at Los Alamos National Laboratory, Toyota Research Institute, and Google Research. Her research interests include combining domain-driven scientific mechanistic modeling with data-driven machine learning methodologies to accelerate and improve spatial and temporal modeling.

Talk Title: Integrating Machine Learning with Physics-Based Spatial and Temporal Modeling

Talk Abstract: Deep learning has achieved great success in numerous areas, and is also seeing increasing interest in scientific applications. However, challenges still remain: scientific phenomena are difficult to model, and can also be limited by a lack of training data. As a result, scientific machine learning approaches are being developed by incorporating domain knowledge into the machine learning process to enable more accurate and general predictions. One such popular approach, colloquially known as physics-informed neural networks (PINNs), incorporates domain knowledge as soft constraints on an empirical loss function. I will discuss the challenges associated with such an approach, and show that by changing the learning paradigm to curriculum regularization or sequence-to-sequence learning, we can achieve significantly lower error. Another approach, colloquially known as ODE-Nets, aims to couple dynamical systems/numerical methods with neural networks. I will discuss how exploiting techniques from numerical analysis for these systems can enable learning continuous, function-to-function mappings for scientific problems.