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

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Bibliografski detalji
Izdano u:	Symmetry vol. 17, no. 9 (2025), p. 1409-1426
Glavni autor:	Kacmaz Semih
Daljnji autori:	Haas, Roland, Huerta, E A
Izdano:	MDPI AG
Teme:	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 pristup:	Citation/Abstract Full Text + Graphics Full Text - PDF
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