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

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Detalhes bibliográficos
Publicado no:	Symmetry vol. 17, no. 9 (2025), p. 1409-1426
Autor principal:	Kacmaz Semih
Outros Autores:	Haas, Roland, Huerta, E A
Publicado em:	MDPI AG
Assuntos:	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
Acesso em linha:	Citation/Abstract Full Text + Graphics Full Text - PDF
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