Explicit Runge-Kutta integration
Fourth order Runge-Kutta method applied to system of linear differential equations
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Fourth order Runge-Kutta method applied to system of linear differential equations
Numerical solutions for librational motion of Mercury obtained by applying Runge-Kutta integration
Runge-Kutta integration for higher order differential equation solution
Runge-kutta integration to approximate system of nonlinear equations by series of linear equations
Runge-kutta integration to approximate a system of nonlinear equations by a series of linear equations
Mumerical integration of nonlinear differential equation using Runge-Kutta method
Here, we consider the adaptive-rank integration of multi-dimensional time-dependent advection-diffusion partial differential equations (PDEs) with variable coefficients. We employ a standard finite-difference method for spatial discretization coupled with high-order diagonally implicit Runge-Kutta temporal schemes. The discrete equation is a generalized Sylvester equation (GSE), which we solve with a projection-based adaptive-rank algorithm structured around two key strategies: (i) constructing dimension-wise subspaces using a novel atypical extended Krylov strategy, and (ii) efficiently solving the basis coefficient matrix with a preconditioned GMRES solver. The low-rank decomposition is performed in 2D using SVD and with high-order SVD (HOSVD) in 3D to represent the tensor in a compressed Tucker format. For d-dimensional problems (here, d = 2 or 3), the computational complexity and memory storage of the approach are found numerically to scale as and $\mathscr{O}(Nr^2) + \mathscr{O} (r^{d+1})$ and $\mathscr{O}(Nr) + \mathscr{O} (r^{d})$, respectively, with the one-dimensional resolution and the maximal rank during the Krylov iteration (which we find to be largely independent of on our numerical examples). We present numerical examples that illustrate the advertised properties of the algorithm.
PIAFS is a finite-difference code to solve the compressible Navier-Stokes equations with chemical heating on Cartesian grids. It models chemical reactions of air (oxygen and carbon dioxide) with ozone subject to radiation. It uses a high-order WENO spatial discretization and explicit Runge-Kutta time integration. It is capable of parallel simulations using MPI. The code is written in C/C++.
Performance characteristics of higher order approximations of Runge-Kutta type
Modified Runge-Kutta analysis of one-dimensional nozzle flow of gas-solid suspension, noting electrostatic effects
Absorption coefficient for lines with combined Doppler and Lorentz broadening calculated, using Runge-Kutta method, continued fraction expansion and Hermite-Gauss quadrature
Fifth-order Runge-Kutta formulas including extensions of Radau, Lobatto, Newton-Cotes and Legendre-Gauss quadratures
Tables for elementary weights of Runge-Kutta formulas of first eight orders and for relations of explicit formulas through order seven
Runge-Kutta type formulas applied to restricted three-body problem achieving high order accuracy by m-fold differentiation and simple transformation
We introduce stage-local partitioned two-step Runge-Kutta methods are an extension of standard two-step Runge-Kutta methods, which are an alternative to the standard additive two-step Runge-Kutta methods currently existing in the literature. Furthermore, these new schemes are designed with an eye towards truly N-partitioned systems and leverage local stage approximations to make several computationally interesting approximations viable. Specifically, the focus on local stage approximations makes possible the construction of truly asynchronous schemes, in the parallel sense, possible. In addition, we show that an implicit-explicit approach to these schemes can lead to methods that require the inversion of only local nonlinear systems.
We propose a method for interpolating divergence-free continuous magnetic fields via vector potential reconstruction using Hermite interpolation, which ensures high-order continuity for applications requiring adaptive, high-order ordinary differential equation (ODE) integrators, such as the Dormand-Prince method. The method provides C(m) continuity and achieves high-order accuracy, making it particularly suited for particle trajectory integration and Poincaré section analysis under optimal integration order and timestep adjustments. Through numerical experiments, we demonstrate that the Hermite interpolation method preserves volume and continuity, which are critical for conserving toroidal canonical momentum and magnetic moment in guiding center simulations, especially over long-term trajectory integration. Furthermore, we analyze the impact of insufficient derivative continuity on Runge-Kutta schemes and show how it degrades accuracy at low error tolerances, introducing discontinuity-induced truncation errors. Lastly, we demonstrate performant Poincaré section analysis in two relevant settings of field data collocated from finite element meshes.
This repository is used to release code generated for manuscripts on the Photosynthetic Energy Transduction core program. This code simulates the reduction of HoxEFU by NADH. The electro transfer rate constants for the simulation are specified in the .csv files. The two .csv files correspond tot he two models described in Dawson et al. Cell. Rep. Phys. Sci. 2026. The code utilizes a chemical master equation, a set of differential equations, defining the time evolution of the oxidation and reduction kinetics of NAD+, NADH, a FMN flavin, and a set of iron sulfur clusters. The kinetics of HoxEFU reduction by NADH are evaluated by numerical integration of the chemical master equation using a variable-time-step Runge-Kutta algorithm.
Recently, a new class of nonlinearly partitioned Runge–Kutta (NPRK) methods was proposed for nonlinearly partitioned systems of autonomous ordinary differential equations y' = F(y, y). The target class of problems are those in which different scales, stiffnesses, or physics are coupled in a nonlinear way, wherein the desired partition cannot be written in a classical additive or component-wise fashion. Here we use a rooted-tree analysis to derive full-order conditions for NPRKM methods, where M denotes the number of nonlinear partitions. Due to the nonlinear coupling and thereby the mixed product differentials, it turns out that the standard node-colored rooted tree analysis used in analyzing ODE integrators does not naturally apply. Instead we develop a new edge-colored rooted-tree framework to address the nonlinear coupling. The resulting order conditions are enumerated, are provided directly for up to fourth order with M = 2 and third order with M = 3, and are related to existing order conditions of additive and partitioned RK methods. We conclude with an example that shows how the nonlinear order conditions can be used to obtain an embedded estimate of the state-dependent nonlinear coupling strength in a dynamical system.