[Sds-seminars] S&DS In-Person Seminar, Lu Lu, 2/27, 4pm-5pm, "Physics-informed deep learning: Blending data and physics for learning functions and operators"

[elizavette.torres at yale.edu]

 <https://statistics.yale.edu/>     <https://statistics.yale.edu/>
Department of Statistics and Data Science  

In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (
<https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f
8b73c34-a27b-42a7-a073-af2d00f90ffa>
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8
b73c34-a27b-42a7-a073-af2d00f90ffa)

 <https://0.0.0.10/> 3:30pm -   Pre-talk meet and greet teatime - Dana
House, 24 Hillhouse Avenue 


Lu Lu, University of Pennsylvania




Date: Monday, February 27, 2023

Time: 4:00PM to 5:00PM

Mason Lab 211
<http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us
> see map 

9 Hillhouse Ave

New Haven, CT 06511

 <https://directory.seas.upenn.edu/lu-lu/> Website

 

 

Title: Physics-informed deep learning: Blending data and physics for
learning functions and operators

 

Information and Abstract: 

Deep learning has achieved remarkable success in diverse applications;
however, its use in scientific applications has emerged only recently. In
this talk, I will first review physics-informed neural networks (PINNs) and
available extensions for solving forward and inverse problems of partial
differential equations (PDEs). I will then introduce a less known but
powerful result that a NN can accurately approximate any nonlinear operator.
This universal approximation theorem of operators is suggestive of the
potential of NNs in learning operators of complex systems. I will present
the deep operator network (DeepONet) to learn various operators that
represent deterministic and stochastic differential equations. I will
demonstrate the effectiveness of DeepONet and its extensions to diverse
multiphysics and multiscale problems, such as nanoscale heat transport,
bubble growth dynamics, high-speed boundary layers, electroconvection,
hypersonics, and geological carbon sequestration. Deep learning models are
usually limited to interpolation scenarios, and I will quantify the
extrapolation complexity and develop a complete workflow to address the
challenge of extrapolation for deep neural operators.

For more details and upcoming events visit our website at
<http://statistics.yale.edu/> http://statistics.yale.edu/

 

Department of Statistics and Data Science

Yale University
24 Hillhouse Avenue
New Haven, CT 06511

t 203.432.0666
f 203.432.0633

 

 

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