Issue 7, 2025

Solving an inverse problem with generative models

Abstract

Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are considered a more challenging problem in science and engineering than forward problems where we compute or measure outputs from known inputs. In this work we demonstrate the use of two generative machine learning methods to solve inverse problems. We compare this approach to two more conventional approaches that use a forward model with nonlinear programming, and the use of a backward model. We illustrate each method on a dataset obtained from a simple remote instrument that has three inputs: the setting of the red, green and blue channels of an RGB LED. We focus on several outputs from a light sensor that measures intensity at 445 nm, 515 nm, 590 nm, and 630 nm. The specific problem we solve is identifying inputs that lead to a specific intensity in three of those channels. We show that generative models can be used to solve this kind of inverse problem, and they have some advantages over the conventional approaches.

Graphical abstract: Solving an inverse problem with generative models

Article information

Article type
Paper
Submitted
03 Apr 2025
Accepted
10 Jun 2025
First published
17 Jun 2025
This article is Open Access
Creative Commons BY license

Digital Discovery, 2025,4, 1856-1869

Solving an inverse problem with generative models

J. R. Kitchin, Digital Discovery, 2025, 4, 1856 DOI: 10.1039/D5DD00137D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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