[UCR_DataScience] [Fwd: May 24: Special Lecture of the UCR Center for Quantitative Modeling in Biology]

[tsotras at cs.ucr.edu]

Please find below an interesting talk on ML for scientific modeling and
simulation.

Sincerely,
V. Tsotras
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Special Lecture of the UCR Center for Quantitative Modeling in Biology
and
Mathematics Colloquium

Hybrid Format

3:30pm, May 24th
284 Skye Hall in person
or
link: https://ucr.zoom.us/j/95137485369

Paul Atzberger
Department of Mathematics
University of California, Santa Barbara

Title: Toward Robust Machine Learning Methods for Scientific Modeling and
Simulation

Abstract:  Recent emerging data-driven methods combined with more
traditional numerical
analysis are presenting new opportunities for model development and for
performing simulations.
We will discuss a few motivating applications in fluid mechanics and
biophysics.  We first discuss
challenges in biophysical modeling of membrane proteins arising from the
roles played by
geometry and transport equations on curved surfaces.  We discuss
development of hybrid
data-driven solvers for partial differential equations on manifolds.  We
show how these
methods can be used to study membrane protein interactions and
drift-diffusion dynamics
taking into account the roles of hydrodynamic coupling, geometry, and
thermal fluctuations.
We then discuss how representations can be learned for non-linear
stochastic dynamics
leveraging recent data-driven methods related to Variational Autoencoders
(VAEs) and
Generative Adversarial Networks (GANs).  We show how these methods can be
used to
develop reduced-order models, dimension reductions, or learn unknown
force-laws.
We present results for partial differential equations in fluid mechanics,
reaction-diffusion
processes, and particle systems.  Throughout, we aim to highlight
opportunities for combining
recent emerging machine learning methods with more traditional numerical
approaches to
develop practical computational methods for scientific modeling and
simulation.
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