4.7 Article

Physics-informed Machine Learning for Modeling Turbulence in Supernovae

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Review Multidisciplinary Sciences

Core-collapse supernova explosion theory

A. Burrows et al.

Summary: Supernova explosions result from the death of massive stars, giving birth to neutron stars and black holes while ejecting solar masses of heavy elements. The delayed neutrino-heating mechanism is emerging as the key driver, but there are still many issues to address, such as the chaotic dynamics involved.

NATURE (2021)

Article Astronomy & Astrophysics

Artificial neural network subgrid models of 2D compressible magnetohydrodynamic turbulence

Shawn G. Rosofsky et al.

PHYSICAL REVIEW D (2020)

Article Astronomy & Astrophysics

Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova Explosions in One Dimension

Sean M. Couch et al.

ASTROPHYSICAL JOURNAL (2020)

Article Astronomy & Astrophysics

Towards an understanding of the resolution dependence of Core-Collapse Supernova simulations

Hiroki Nagakura et al.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2019)

Review Physics, Multidisciplinary

Machine learning and the physical sciences

Giuseppe Carleo et al.

REVIEWS OF MODERN PHYSICS (2019)

Review Physics, Nuclear

Turbulence in core-collapse supernovae

David Radice et al.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2018)

Review Astronomy & Astrophysics

Crucial Physical Dependencies of the Core-Collapse Supernova Mechanism

A. Burrows et al.

SPACE SCIENCE REVIEWS (2018)

Article Physics, Fluids & Plasmas

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

Jin-Long Wu et al.

PHYSICAL REVIEW FLUIDS (2018)

Article Physics, Fluids & Plasmas

Autonomic closure for turbulence simulations

Ryan N. King et al.

PHYSICAL REVIEW E (2016)

Article Multidisciplinary Sciences

A large-scale dynamo and magnetoturbulence in rapidly rotating core-collapse supernovae

Philipp Moesta et al.

NATURE (2015)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Review Astronomy & Astrophysics

Large-Eddy Simulations of Magnetohydrodynamic Turbulence in Heliophysics and Astrophysics

Mark Miesch et al.

SPACE SCIENCE REVIEWS (2015)

Article Astronomy & Astrophysics

BEYOND MIXING-LENGTH THEORY: A STEP TOWARD 321D

W. David Arnett et al.

ASTROPHYSICAL JOURNAL (2015)

Review Astronomy & Astrophysics

Astrophysical Hydromagnetic Turbulence

A. Brandenburg et al.

SPACE SCIENCE REVIEWS (2013)

Article Astronomy & Astrophysics

Semi-global simulations of the magneto-rotational instability in core collapse supernovae

M. Obergaulinger et al.

ASTRONOMY & ASTROPHYSICS (2009)

Article Astronomy & Astrophysics

Late-time convection in the collapse of a 23M⊙ star

Christopher L. Fryer et al.

ASTROPHYSICAL JOURNAL (2007)

Review Physics, Multidisciplinary

The physics of core-collapse supernovae

S Woosley et al.

NATURE PHYSICS (2005)

Article Astronomy & Astrophysics

Stability of standing accretion shocks, with an eye toward core-collapse supernovae

JM Blondin et al.

ASTROPHYSICAL JOURNAL (2003)