arXiv Open Access 2023

Physics-informed machine learning of the correlation functions in bulk fluids

Wenqian Chen Peiyuan Gao Panos Stinis

Lihat Sumber

Abstrak

The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.

Topik & Kata Kunci

physics.comp-ph cs.LG physics.chem-ph physics.flu-dyn

Penulis (3)

Wenqian Chen

Peiyuan Gao

Panos Stinis

Format Sitasi

APA MLA BibTeX

Chen, W., Gao, P., Stinis, P. (2023). Physics-informed machine learning of the correlation functions in bulk fluids. https://arxiv.org/abs/2309.00767

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓