Machine Learning-Driven Conservative-to-Primitive Conversion in Hybrid Piecewise Polytropic and Tabulated Equations of State

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Detalles Bibliográficos
Publicado en:	Symmetry vol. 17, no. 9 (2025), p. 1409-1426
Autor principal:	Kacmaz Semih
Otros Autores:	Haas, Roland, Huerta, E A
Publicado:	MDPI AG
Materias:	Accuracy Relativistic effects Neutrons Neutron stars Artificial neural networks Supercomputers Numerical analysis Hydrodynamics Theory of relativity Machine learning Performance evaluation Numerical methods Data points Simulation Datasets Graphics processing units Equations of state Neural networks Optimization Inference Variables Spacetime Methods Algorithms Fluid mechanics Parameter estimation
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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