Results 111 to 120 of about 11,954,654 (221)

Multi-step Runge-Kutta methods [PDF]

, 1968
Multi-step Runge-Kutta methods using method of ...
Rosen, J. S.
core  

Edge‐Based Discretizations on Triangulations in Any Dimension, With Special Attention to Four‐Dimensional Space

International Journal for Numerical Methods in Fluids, Volume 98, Issue 10, Page 1143-1164, October 2026.
This article provides important geometric formulas for node‐centered, edge‐based schemes in any number of dimensions. These formulas are noteworthy, as they do not require the explicit formation of dual regions. We prove several key geometric results, with a particular focus on the four‐dimensional case, due to potential space‐time applications ...
Nicholas Tufillaro, David M. Williams, Hiroaki Nishikawa   +2 more
wiley   +1 more source

Subspace Acceleration for Efficient Nonlinear Water Wave Simulation

International Journal for Numerical Methods in Fluids, Volume 98, Issue 10, Page 1165-1180, October 2026.
We introduce an exponentially weighted subspace acceleration technique to reduce GMRES iterations for solving the Poisson equation with time‐dependent coefficients in nonlinear, dispersive free‐surface flows governed by the incompressible Navier‐Stokes equations. The method significantly reduces memory requirements and computational complexity compared
Rasmus Kleist Hørlyck Sørensen, Margherita Guido, Allan Peter Engsig‐Karup, Daniel Kressner   +3 more
wiley   +1 more source

QG‐Python: A Reproducible 1.5‐Layer Quasi‐Geostrophic Modelling Service With Direct Sparse Solvers, Spectral Stability Diagnostics and Filter Sensitivity Data

Geoscience Data Journal, Volume 13, Issue 4, October 2026.
QG‐Python is an open, reproducible 1.5‐layer quasi‐geostrophic modelling service whose published diagnostics show that the classical von Neumann bound underestimates the true Leapfrog stability limit by a factor of 13.7, and that the Robert–Asselin–Williams filter reduces kinetic energy and enstrophy biases from ~21% and ~45% to under 4%.
Elias D. Nino‐Ruiz
wiley   +1 more source

Efficient Numerical Method for Solving a Quadratic Riccati Differential Equation

Abstract and Applied Analysis
This study presents families of the fourth-order Runge–Kutta methods for solving a quadratic Riccati differential equation. From these families, the England version is more efficient than other fourth-order Runge–Kutta methods and practically well-suited
Wendafrash Seyid Yirga, Fasika Wondimu Gelu, Wondwosen Gebeyaw Melesse, Gemechis File Duressa   +3 more
doaj   +1 more source

Multi‐Objective Bayesian Co‐Optimization of Parameterized Moving Horizon Estimation and Model Predictive Control

International Journal of Robust and Nonlinear Control, Volume 36, Issue 15, Page 7193-7213, October 2026.
ABSTRACT This paper proposes a Machine Learning (ML)‐enabled estimator‐controller design framework, in which a parameterized Model Predictive Controller (MPC) and a parameterized Moving Horizon Estimator (MHE) are jointly refined using Bayesian Optimization (BO).
Hossein Nejatbakhsh Esfahani, Javad Mohammadpour Velni   +1 more
wiley   +1 more source

An Improved Fifth-Order Runge-Kutta Method with Higher Accuracy and Efficiency for Solving Initial Value Problems

Science and Technology Indonesia
Solving initial value problems (IVPs) in ordinary differential equations (ODEs) often requires numerical methods, with the fifth-order Runge-Kutta method being a widely used approach due to its balance between accuracy and computational efficiency.
Ummu Habibah, Fermin Franco Medrano, Adith Chandra Permana, Dita Ardiana, Trisilowati   +4 more
doaj   +1 more source

FUZZY DELAY DIFFERENTIAL EQUATIONS WITH HYBRID SECOND AND THIRD ORDERS RUNGE-KUTTA METHOD [PDF]

Journal of Engineering Science and Technology, 2018
This paper considers fuzzy delay differential equations with known statedelays. A dynamic problem is formulated by time-delay differential equations and an efficient scheme using a hybrid second and third orders Runge-Kutta method is developed and ...
RUI SIH LIM, SU HOE YEAK, ROHANIN AHMAD
doaj  

How Much Physics Should a Neural Network Know? Thermodynamics‐Informed Neural Networks for Rock Constitutive Modeling With Epistemic Uncertainty

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Machine learning offers a flexible route to constitutive modeling, with two emergent questions for geoscience applications: what level of thermodynamic constraint should be embedded in the network architecture, and how should predictive uncertainty be quantified when extrapolating from laboratory to field conditions? We address both challenges
Kangan Li, Tushar Mittal, Benjamin K. Holtzman, Seth Saltiel   +3 more
wiley   +1 more source

A Stochastic Model for Fog Forecasting

Geophysical Research Letters, Volume 53, Issue 17, 16 September 2026.
Abstract Despite significant advancements in parameterizations of boundary layer processes, forecasting, and nowcasting low‐level clouds using numerical models remain challenging. The purpose of this study is to test a prototype of a high‐resolution stochastic‐deterministic model designed to simulate the life cycle of fog cover based on the Ising model
E. Cardoso‐Bihlo, B. Khouider
wiley   +1 more source