Results 51 to 60 of about 13,068 (272)
Superradiance and Broadband Emission Driving Fast Electron Dephasing in Open Quantum Systems
Advanced Science, EarlyView.We uncover the physical origin of ultrafast electron dephasing in solid‐state high‐harmonic generation by simulating the Lindblad equation for the dissipative Hubbard model. Coexistence of Dicke superradiance and broadband emission is revealed, whose destructive interference shortens the effective scattering time and provides a unified picture of ...
Gimin Bae, Youngjae Kim, Jae Dong Lee
wiley +1 more source
Advanced Electronic Materials, EarlyView.Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa +6 more
wiley +1 more source
Factorized Runge–Kutta–Chebyshev Methods
Journal of Physics: Conference Series, 2017 The second-order extended stability Factorized Runge-Kutta-Chebyshev (FRKC2) class of explicit schemes for the integration of large systems of PDEs with diffusive terms is presented. FRKC2 schemes are straightforward to implement through ordered sequences of forward Euler steps with complex stepsizes, and easily parallelised for large scale problems on
openaire +3 more sources
On Order Conditions for modified Patankar-Runge-Kutta schemes [PDF]
, 2017 In [6] the modified Patankar–Euler and modified Patankar–Runge–Kutta schemes were introduced to solve positive and conservative systems of ordinary differential equations.
S. Kopecz, A. Meister
semanticscholar +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Alexandria Engineering Journal, 2016 This paper reflects some research outcome denoting as to how Lotka–Volterra prey predator model has been solved by using the Runge–Kutta–Fehlberg method (RKF).
Susmita Paul +2 more
doaj +1 more source
On strong stability of explicit Runge–Kutta methods for nonlinear semibounded operators [PDF]
IMA Journal of Numerical Analysis, 2018 Explicit Runge–Kutta methods are classical and widespread techniques in the numerical solution of ordinary differential equations (ODEs). Considering partial differential equations, spatial semidiscretizations can be used to obtain systems of ODEs that
Hendrik Ranocha
semanticscholar +1 more source
Controlling Dynamical Systems Into Unseen Target States Using Machine Learning
Advanced Intelligent Systems, EarlyView.Parameter‐aware next‐generation reservoir computing enables efficient, data‐driven control of dynamical systems across unseen target states and nonstationary transitions. The approach suppresses transient behavior while navigating system collapse scenarios with minimal training data—over an order of magnitude less than traditional methods.
Daniel Köglmayr +2 more
wiley +1 more source
Positivity of an explicit Runge–Kutta method
Ain Shams Engineering Journal, 2015 This paper deals with the numerical solution of initial value problems (IVPs), for systems of ordinary differential equations (ODEs), by an explicit fourth-order Runge–Kutta method (we will refer to it as the classical fourth-order method) with special ...
M. Mehdizadeh Khalsaraei
doaj +1 more source
Economical Runge-Kutta methods
Rendiconti di Matematica e delle Sue Applicazioni, 1995 This paper deals with explicit Runge-Kutta methods of the type \(y_{n + 1} = y_ n + h \sum^ s_{i = 2} b_ i K^ n_ i\), \(K^ n_ i = f(x_ n + c_ ih, y_ n + ha_{i1} K^{n-1}_ s + h \sum^{i - 1}_{j = 2} a_{ij} K^ n_ j)\), with \(b_ 1 = 0\), \(c_ s = 1\). By using information from the previous step one function evaluation is saved.
Costabile Francesco +2 more
openaire +3 more sources