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

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Bibliografiske detaljer
Udgivet i:	Symmetry vol. 17, no. 9 (2025), p. 1409-1426
Hovedforfatter:	Kacmaz Semih
Andre forfattere:	Haas, Roland, Huerta, E A
Udgivet:	MDPI AG
Fag:	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
Online adgang:	Citation/Abstract Full Text + Graphics Full Text - PDF
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