Structure Exploiting Trust Regions for Bilevel and Risk-Averse Optimization

Abstract

George Mason University and Sandia National Labs are working on the same project with different project numbers. Our team in this project initiated the development and theoretical analysis of algorithms for non smooth optimization problems constrained by large-scale models, such as systems of partial differential equations (PDEs). The team considered a large number of application areas including data science, deep neural networks, data assimilation, diffusion maps, reduced order modeling, materials science, imaging, fluid dynamics, shape optimization, fractional (nonlocal) models, free boundary problems, etc. Below, we have summarized the achievements made during the three years of the project.

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Document Details

Document Type
Technical Report
Publication Date
Jan 13, 2022
Accession Number
AD1230495

Entities

People

  • Harbir Antil

Organizations

  • George Mason University

Tags

Readers

  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Operations Research
  • Research Science/Academic Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms