Loading…

Random field optimization

•Present modeling abstraction that we call random field optimization.•Abstraction captures uncertainty that lives on continuous space-time domains.•Demonstrate the applicability using case studies. [Display omitted] We present a new modeling paradigm for optimization that we call random field optimi...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering 2022-09, Vol.165 (C), p.107854, Article 107854
Main Authors: Pulsipher, Joshua L., Davidson, Benjamin R., Zavala, Victor M.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Present modeling abstraction that we call random field optimization.•Abstraction captures uncertainty that lives on continuous space-time domains.•Demonstrate the applicability using case studies. [Display omitted] We present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of random variables that live on infinite-dimensional spaces (e.g., space and time) such as stochastic processes (e.g., time series, Gaussian processes, and Markov processes), random matrices, and random spatial fields. This paradigm involves sophisticated mathematical objects (e.g., stochastic differential equations and space-time kernel functions) and has been widely used in neuroscience, geoscience, physics, civil engineering, and computer graphics. Despite of this, however, random fields have seen limited use in optimization; specifically, existing optimization paradigms that involve uncertainty (e.g., stochastic programming and robust optimization) mostly focus on the use of finite random variables. This trend is rapidly changing with the advent of statistical optimization (e.g., Bayesian optimization) and multi-scale optimization (e.g., integration of molecular sciences and process engineering). Our work extends a recently-proposed abstraction for infinite-dimensional optimization problems by capturing more general uncertainty representations. Moreover, we discuss solution paradigms for this new class of problems based on finite transformations and sampling, and identify open questions and challenges.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2022.107854