Work

Implementation of the emulator-based component analysis

Year: 2024

Type: article

Abstract: We present a PyTorch-powered implementation of the emulator-based component analysis used for ill-posed numerical non-linear inverse problems, where an approximate emulator for the forward problem is known. This emulator may be a numerical model, an interpolating function, or a fitting function such as a neural network. With the help of the emulator and a data set, the method seeks dimensionality reduction by projection in the variable space so that maximal variance of the target (response) values of the data is covered. The obtained basis set for projection in the variable space defines a subspace of the greatest response for the outcome of the forward problem. The method allows for the reconstruction of the coordinates in this subspace for an approximate solution to the inverse problem. We present an example of using the code provided as a Python class. • Inverse problems appear in many fields of research. • Machine learning can provide a tool for solving these problems. • Solution can be approximated using a dimensionality reduction approach. • An efficient algorithm allows estimating a solution for complex systems.

Source: Journal of Computational Science

DOI: 10.1016/j.jocs.2024.102437

Authors: Anton Vladyka, Eemeli A. Eronen, Johannes Niskanen

Institution: University of Turku

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,890

Funders: Academy of Finland, China Scholarship Council, Jenny ja Antti Wihurin Rahasto