[Colloquium] Andrew Stuart (Caltech) - Colloquium (4/21) and Mini-Course (4/25-26)

[Rob Mitchum]

Andrew Stuart <http://stuart.caltech.edu/>, Bren Professor of Computing and
Mathematical Sciences at Caltech, will present a colloquium and a
mini-course this month, sponsored by the Committee on Computational and
Applied Mathematics, the DSI AI + Science Initiative, and the National
Science Foundation. The host for these events is Daniel Sanz-Alonso
<https://stat.uchicago.edu/people/profile/daniel-sanz-alonso/> of
Statistics and the Committee on Computational and Applied Mathematics.

*Colloquium: Learning Operators*
Thursday, April 21st
4:00 - 5:00 PM
Jones 303

Consider map F: U –> V. Given data pairs {u_j,F(u_j)} the goal of
supervised learning is to approximate F. Neural networks have shown
considerable success in addressing this problem in settings where X is a
finite dimensional Euclidean space and where Y is either a finite
dimensional Euclidean space (regression) or a set of finite cardinality
(classification). Motivated by the need for surrogate modeling and for
scientific discovery, we focus on the design and analysis of algorithms
which address supervised learning for settings where U and V comprise
spaces of functions; thus F is an operator. The talk describes emerging
methodology in this area, emerging theory which underpins the methodology
and numerical experiments which elucidate the efficiency of different
approaches. Various applications from continuum mechanics are described,
including the Navier-Stokes equation, the Helmholtz equation, nonlinear
elasticity and the advection equation.

*Mini-Course: Ensemble Kalman Methods: A Mean Field Perspective*
Monday, April 25th and Tuesday, April 26th
4:30 - 6:00 PM
Eckhart 206 (Mon) & Eckhart 202 (Tues)

The ensemble Kalman methodology is an innovative and flexible set of tools
which can be used for both state estimation in dynamical systems and
parameter estimation for generic inverse problems. It has primarily been
developed by practitioners in the geophysical sciences, with notable impact
on the fields of oceanography, oil reservoir simulation and weather
forecasting. Despite its wide adoption in the geosciences, the methodology
is hard to analyze and firm theoretical foundations are only now starting
to emerge. The purpose of this talk is to provide a unifying mean field
perspective on the subject area. This perspective helps to clarify
inter-relations within the existing literature, and provides a framework
within which open problems may be addressed.

*Bio*: Andrew Stuart has research interests in applied and computational
mathematics, and is interested in particular in the question of how to
optimally combine complex mechanistic models with data. He joined Caltech
in 2016 as Bren Professor of Computing and Mathematical Sciences, after 17
years as Professor of Mathematics at the University of Warwick (1999–2016).
Prior to that he was on the faculty in The Departments of Computer Science
and Mechanical Engineering at Stanford University (1992–1999), and in the
Mathematics Department at Bath University (1989–1992). He obtained his PhD
from the Oxford University Computing Laboratory in 1986, and held
postdoctoral positions in Mathematics at Oxford University and at MIT in
the period 1986–1989.

For further information and inquiries about building access for persons
with disabilities, please contact Zellencia Harris, zellenciah at uchicago.edu.

-Rob



-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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