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Physics-enhanced neural networks for equation-of-state calculations

التفاصيل البيبلوغرافية
العنوان: Physics-enhanced neural networks for equation-of-state calculations
المؤلفون: Timothy J Callow, Jan Nikl, Eli Kraisler, Attila Cangi
المصدر: Machine Learning: Science and Technology, Vol 4, Iss 4, p 045055 (2023)
بيانات النشر: IOP Publishing, 2023.
سنة النشر: 2023
المجموعة: LCC:Computer engineering. Computer hardware
LCC:Electronic computers. Computer science
مصطلحات موضوعية: equation-of-state calculations, average-atom models, physics-enhanced neural networks, warm dense matter, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95
الوصف: Rapid access to accurate equation-of-state (EOS) data is crucial in the warm-dense matter (WDM) regime, as it is employed in various applications, such as providing input for hydrodynamic codes to model inertial confinement fusion processes. In this study, we develop neural network models for predicting the EOS based on first-principles data. The first model utilises basic physical properties, while the second model incorporates more sophisticated physical information, using output from average-atom (AA) calculations as features. AA models are often noted for providing a reasonable balance of accuracy and speed; however, our comparison of AA models and higher-fidelity calculations shows that more accurate models are required in the WDM regime. Both the neural network models we propose, particularly the physics-enhanced one, demonstrate significant potential as accurate and efficient methods for computing EOS data in WDM.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2632-2153
Relation: https://doaj.org/toc/2632-2153
DOI: 10.1088/2632-2153/ad13b9
URL الوصول: https://doaj.org/article/38ca4adca15b40f08a80d4286ebfe223
رقم الأكسشن: edsdoj.38ca4adca15b40f08a80d4286ebfe223
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:26322153
DOI:10.1088/2632-2153/ad13b9