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

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Bibliografske podrobnosti
izdano v:	Symmetry vol. 17, no. 9 (2025), p. 1409-1426
Glavni avtor:	Kacmaz Semih
Drugi avtorji:	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 dostop:	Citation/Abstract Full Text + Graphics Full Text - PDF
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