Work

Physics-driven deep learning for studying strongly interacting systems

Year: 2025

Type: conference-paper

Abstract: In this proceeding, we introduce physics-driven deep learning for the study of strongly interacting systems. We demonstrate that the integration of deep models with physical principles can lead to more efficient and reliable solutions, learned from both first-principles computations and observational data. Specific applications include hadron-hadron interactions and the reconstruction of neutron star equations of state (EoSs). Deep neural networks (DNNs) are employed to represent physical quantities with known symmetries and rules, trained to optimally describe observational data.

DOI: 10.22323/1.483.0257

Author: Lingxiao Wang

Institution: RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences

Language: English

Topic: Quantum Chromodynamics and Particle Interactions

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Funders: Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science, Japan Science and Technology Agency, RIKEN

OpenAlex ID: W4415520767

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