Results 51 to 60 of about 81,957 (247)
Implicit-Explicit Runge-Kutta schemes for numerical discretization of optimal control problems
, 2012 Implicit-explicit (IMEX) Runge-Kutta methods play a major rule in the numerical treatment of differential systems governed by stiff and non-stiff terms.
Herty, Michael +2 more
core +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
Preconditioning of fully implicit Runge-Kutta schemes for parabolic PDEs [PDF]
Modeling, Identification and Control, 2006 Recently, the authors introduced a preconditioner for the linear systems that arise from fully implicit Runge-Kutta time stepping schemes applied to parabolic PDEs (9).
Gunnar A. Staff +2 more
doaj +1 more source
Rosenbrock Type Methods for Solving Non-Linear Second-Order in Time Problems
Mathematics, 2021 In this work, we develop a new class of methods which have been created in order to numerically solve non-linear second-order in time problems in an efficient way.
Maria Jesus Moreta
doaj +1 more source
FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs [PDF]
, 2011 FATODE is a FORTRAN library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities. The paper describes the capabilities, implementation, code organization, and usage of this package.
Sandu, Adrian, Zhang, Hong
core +1 more source
Runge–Kutta methods and renormalization [PDF]
The European Physical Journal C, 2000 A connection between the algebra of rooted trees used in renormalization theory and Runge-Kutta methods is pointed out. Butcher's group and B-series are shown to provide a suitable framework for renormalizing a toy model of field the ory, following Kreimer's approach.
openaire +2 more sources
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
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
Effective order strong stability preserving Runge–Kutta methods [PDF]
, 2012 We apply the concept of effective order to strong stability preserving (SSP) explicit Runge–Kutta methods. Relative to classical Runge–Kutta methods, effective order methods are designed to satisfy a relaxed set of order conditions, but yield higher ...
Hadjimichael, Y. +3 more
core
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