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of 3 869
pro vyhledávání: '"P. McDevitt"'
This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [C. McDevitt et al., A physics-constrained deep learning treatment of runaway electron dynamics, Submitted to Physics of Plasmas (2024)] to ac
Externí odkaz:
http://arxiv.org/abs/2412.13044
An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem i
Externí odkaz:
http://arxiv.org/abs/2412.12980
We explain how the disparate kinematics of quantum mechanics (finite-dimensional Hilbert space of QM) and special relativity (Minkowski spacetime from the Lorentz transformations of SR) can both be based on one principle (relativity principle). This
Externí odkaz:
http://arxiv.org/abs/2404.13064
A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN) to learn the parametric solution of the adjoint to the relativistic F
Externí odkaz:
http://arxiv.org/abs/2403.04948
A reduced kinetic method (RKM) with a first-principle collision operator is introduced in a 1D2V planar geometry and implemented in a computationally inexpensive code to investigate non-local ion heat transport in multi-species plasmas. The RKM succe
Externí odkaz:
http://arxiv.org/abs/2403.03595
Publikováno v:
Phys. Plasmas 31 (2024) 062701
A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the
Externí odkaz:
http://arxiv.org/abs/2402.08495
The exponential growth (avalanching) of runaway electrons (REs) during a tokamak disruption continues to be a large uncertainty in RE modeling. The present work investigates the impact of tokamak geometry on the efficiency of the avalanche mechanism
Externí odkaz:
http://arxiv.org/abs/2401.11291
Autor:
Maytham Hussein, Zhisen Kang, Stephanie L. Neville, Rafah Allobawi, Varsha Thrombare, Augustine Jing Jie Koh, Jonathan Wilksch, Simon Crawford, Mudher Khudhur Mohammed, Christopher A. McDevitt, Mark Baker, Gauri G. Rao, Jian Li, Tony Velkov
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract This untargeted metabolomics study investigated the synergistic antibacterial activity of polymyxin B and Leu10-teixobactin, a depsipeptide inhibitor of cell wall biosynthesis. Checkerboard microdilution assays revealed a significant synergy
Externí odkaz:
https://doaj.org/article/65aae9e3f2a849c3b03fce0edc5edfad
Autor:
McDevitt, Christopher
Publikováno v:
Phys. Plasmas 30 (2023) 092501
A challenging aspect of the description of a tokamak disruption is evaluating the hot tail runaway electron (RE) seed that emerges during the thermal quench. This problem is made challenging due to the requirement of describing a strongly non-thermal
Externí odkaz:
http://arxiv.org/abs/2306.13224
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Accurate detection and tracking of animals across diverse environments are crucial for studying brain and behavior. Recently, computer vision techniques have become essential for high-throughput behavioral studies; however, localizing animal
Externí odkaz:
https://doaj.org/article/965206f2b94f43f9aa93d8d762c26463