Results 151 to 160 of about 335,151 (313)

Using physics‐informed neural networks to quantify submarine groundwater discharge under high‐frequency tidal dynamics using heat as a tracer

open access: yesLimnology and Oceanography: Methods, EarlyView.
Abstract Estimating exchange rates of submarine groundwater discharge (SGD) at high temporal resolution over extended periods remains challenging, particularly when using heat as a tracer in highly dynamic environments such as tidal systems. Currently available heat transport models struggle to accurately quantify SGD exchange rates in these settings ...
S. Frei   +3 more
wiley   +1 more source

Dispersion‐Less Dissipative Soliton Fiber Laser

open access: yesLaser &Photonics Reviews, EarlyView.
A dispersion‐less fiber laser architecture generates high‐energy, pedestal‐free picosecond pulses without resorting to conventional pulse stretching. This energy‐managed laser achieves remarkable flexibility in pulse parameters, delivering up to 0.54 μJ$\mathrm{\mu}\mathrm{J}$ pulses with minimal spectral distortion using standard telecom components ...
Mostafa I. Mohamed   +2 more
wiley   +1 more source

A highly accurate numerical method for solving boundary value problem of generalized Bagley‐Torvik equation

open access: yesMathematical Methods in the Applied Sciences, EarlyView.
A highly accurate numerical method is given for the solution of boundary value problem of generalized Bagley‐Torvik (BgT) equation with Caputo derivative of order 0<β<2$$ 0<\beta <2 $$ by using the collocation‐shooting method (C‐SM). The collocation solution is constructed in the space Sm+1(1)$$ {S}_{m+1}^{(1)} $$ as piecewise polynomials of degree at ...
Suzan Cival Buranay   +2 more
wiley   +1 more source

On Bounds for Norms of Reparameterized ReLU Artificial Neural Network Parameters: Sums of Fractional Powers of the Lipschitz Norm Control the Network Parameter Vector

open access: yesMathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT It is an elementary fact in the scientific literature that the Lipschitz norm of the realization function of a feedforward fully connected rectified linear unit (ReLU) artificial neural network (ANN) can, up to a multiplicative constant, be bounded from above by sums of powers of the norm of the ANN parameter vector.
Arnulf Jentzen, Timo Kröger
wiley   +1 more source

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