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Data-driven Discovery of the Governing Equations of Complex Physical Systems
Date: June 20th 3:00pm at Bldg 53 Rm 4002
Speaker: Paulo Alves
The increasing rate of production of scientific data, from both high-rep-rate experiments and large supercomputer simulations, is stimulating new opportunities in the way we do science. I will discuss how modern machine learning (ML) regression techniques can be exploited to uncover accurate and interpretable physical models directly from (high-fidelity simulation or experimental) data of complex physical systems. In particular, I will show how recent sparse learning methods can be used to discover partial differential equations (PDEs) that describe the spatio-temporal dynamics of the data in an interpretable form. The potential of these technqiues will be demonstrated by extracting reduced physical models of plasma dynamics from high-fidelity Particle-in-Cell (PIC) simulations. Plasmas provide a complex and challenging test bed for data-driven discovery techniques due to their multi-scale and multivariate dynamics. I will demonstrate the recovery of the fundamental hierarchy of plasma physics equations, from the kinetic Vlasov equation to magnetohydrodynamics, based solely on spatial and temporal data of plasma dynamics from first-principles PIC simulations. The challenges associated with correlated plasma variables and the noise intrinsic to PIC simulation data will also be presented, and I will discuss strategies to overcome these issues for the robust recovery of the underlying plasma dynamics. I will conclude with an outlook on how such data-driven model discovery techniques can accelerate scientific research of complex physical systems.
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