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

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Pubblicato in:	Symmetry vol. 17, no. 9 (2025), p. 1409-1426
Autore principale:	Kacmaz Semih
Altri autori:	Haas, Roland, Huerta, E A
Pubblicazione:	MDPI AG
Soggetti:	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
Accesso online:	Citation/Abstract Full Text + Graphics Full Text - PDF
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