VAIM for Solving Inverse Problems

Presenter and Co-Authors

Manal Almaeen, Old Dominion UniversityFollow
Yasir Alanazi, Old Dominion University
Michelle Kuchera
Nobuo Sato
Wally Melnitchouk
Yaohang Li, Old Dominion University

College

College of Sciences

Department

Computer Science

Program

Computer Science

Publication Date

4-2021

DOI

10.25883/1qk9-nh04

Abstract

In this work, we propose the Variational Autoencoder Inverse Mapper (VAIM) to solve inverse problems, where there is a demand to accurately restore hidden parameters from indirect observations. VAIM is an autoencoder-based neural network architecture. The encoder and decoder networks approximate the forward and backward mapping, respectively, and a variational latent layer is incorporated into VAIM to learn the posterior parameter distributions with respect to the given observables. VAIM shows promising results on several artificial inverse problems. VAIM further demonstrates preliminary effectiveness in constructing the inverse function mapping quantum correlation functions to observables in a quantum chromodynamics analysis of nucleon structure and hadronization.

Disciplines

Computer Sciences

Files

Recommended Citation

Almaeen, Manal; Alanazi, Yasir; Kuchera, Michelle; Sato, Nobuo; Melnitchouk, Wally; and Li, Yaohang, "VAIM for Solving Inverse Problems" (2021). College of Sciences Posters. 6.
https://digitalcommons.odu.edu/gradposters2021_sciences/6