Chandrajit Bajaj
University of Texas at Austin
Learning Models from Data with Non-Convex Optimization
View the recordings of all the lectures on our YouTube Playlist, or select a specific presentation below.
About
Dates: February 22 - February 25 (2018)
Location: University of South Carolina
The 2018 Spring School features six lecturers who are considered high-caliber representatives of their respective area of expertise. The lectures are tutorial in nature and are interlaced with panel discussions and small group discussions allowing participants to actively engage.
The content being covered is organized under our main overarching theme, namely to foster synergetic syntheses of, on the one hand, classical “model based” and, on the other hand, “data-driven” methodologies such as forward and inverse tasks in Uncertainty Quantification, parameter and state estimation, data assimilation, machine learning, structural imaging in material science, and modeling.
We plan to pair these topics with recent methodological developments, in particular those that are able to cope with the challenge of spatial high-dimensionality shared by all the above topics. Examples, to name a few, are sparse high-dimensional polynomial expansions, low-rank and tensor methods, certifiable model order reduction concepts, and sparsity promoting regularization concepts and greedy strategies.
Invited Speakers
Wolfgang Dahmen
University of South Carolina
Data and Models: Introduction
Ronald A. Devore
Texas A&M
Approximation and Data Assimilation
Benjamin Berkels
RWTH Aachen University
An Introduction to Variational Image Processing
Leszek Demkowicz
University of Texas at Austin
Discontinuous Petrov-Galerkin (DPG) Method with Optimal Test Functions
Jay Gopalakrishnan
Portland State University
Discontinuous Petrov-Galerkin (DPG) Method with Optimal Test Functions
Nicholas Dexter
University of Tennessee
Joint-Sparse Approximation for High-Dimensional Parameterized PDEs
Yeongjong Shin
Ohio State University
Function Approximation with Oversampling
Siming He
University of Maryland
Multi-Scale and Multi-Species Modeling for Collective Behavior