1,828 research outputs found
Nonlinear dynamics in superlattices driven by high frequency ac-fields
We investigate the dynamical processes taking place in nanodevices driven by
high-frequency electromagnetic fields. We want to elucidate the role of
different mechanisms that could lead to loss of quantum coherence. Our results
show how the dephasing effects of disorder that destroy after some periods
coherent oscillations, such as Rabi oscillations, can be overestimated if we do
not consider the electron-electron interactions that can reduce dramatically
the decoherence effects of the structural imperfections. Experimental
conditions for the observation of the predicted effects are discussed.Comment: REVTEX (8 pages) and 4 figures (Postscript
Validating reduced turbulence model predictions of electron temperature gradient transport on a JET improved-confinement scenario
Validating reduced turbulence model predictions of electron temperature gradient transport on a JET improved-confinement scenario
Are the Secrecy Order Compensation Provisions of the Patent Act Constitutional Under the Fifth Amendment?
The secrecy order provisions of the Patent Act\u27 raise a number of issues under the U.S. Constitution. The primary focus of this note is on the Fifth Amendment issues raised by the Invention Secrecy Act
An active learning pipeline for surrogate models of gyrokinetic turbulence
Digital twinning of a tokamak device requires fast system state inference. Physics-based computational models that predict future states are often too slow to be actionable, and thus undesirable for offline scenario planning. These tasks may be performed faster if the physics-based model is replaced by a neural network-based surrogate. Obtaining the labels to train the surrogate can be computationally expensive, additionally, some inputs may result in trivial outputs. Here we propose a two-stage active learning pipeline for digital twinning of gyrokinetic turbulence in the core of tokamak fusion plasmas. Our pipeline leverages an uncertainty-based acquisition function which greatly outperforms random acquisition and leads to a reduction of 99.6% in the amount of input-output mappings needed from the physical model without compromising on performance.</p
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