Scientific Machine Learning: Opportunities and Challenges - Keynote
Explore the fascinating world of Scientific Machine Learning (SciML) in this keynote address from JuliaCon 2020. Delve into the opportunities and challenges of integrating machine learning with scientific computing, examining how physics-based models and big data intersect in computational science. Learn about complex multiscale phenomena, high-dimensional parameters, and the challenges of sparse, intrusive data. Discover innovative approaches like the Lift & Learn method, illustrated through a rocket engine combustor example. Gain insights into model reduction techniques, their similarities and differences with machine learning, and how they can be combined for optimal results. Conclude with a vision of the future of computational science and programming languages, followed by a Q&A session addressing practical advice, potential pitfalls, and the impact of SciML on non-linear systems.