Contributed talk
2026 workshop program
Identifying Spacetimes using Neural Networks
The work
Abstract
In general relativity, determining whether two spacetime metric solutions expressed in different gauge describe the same physical scenario poses a significant challenge. This study proposes a novel approach to addressing this problem within the context of numerical relativity by leveraging neural networks. Specifically, we introduce the first implementation of neural networks trained to learn the coordinate mapping between two metric solutions that share identical manifold structure. I will also discuss how this approach could be used to compare various numerical relativity codes, where different choice of gauge and coordinates can affect the final form of the results.