Research project
Multi-Task Symbolic Regression for Gravitational Wave Law Discovery
Developed transformer-based symbolic regression models using contrastive learning to uncover physical laws from gravitational wave data.
This project was conducted as a research internship at Radboud University, in the High Energy Physics group under the supervision of Prof. Sascha Caron, Nijmegen, Netherlands, from June 2025 to September 2025.
Project Overview
This research explored multi-task symbolic regression using transformer architectures to uncover interpretable physical relations in gravitational wave data.
The approach leveraged contrastive learning to evaluate the likelihood of observed numerical data under candidate symbolic expressions using transformers.
Keywords
Deep Learning · Transformers · Symbolic Regression · Contrastive Learning · Python · Gravitational Wave Analysis