Euler Method Research Articles

Structure‐preserving numerical methods for Fokker–Planck equations

AbstractA common way to numerically solve Fokker–Planck equations is the Chang–Cooper method in space combined with one of the Euler methods in time. However, the explicit Euler method is only conditionally positive, leading to severe restrictions on the time step to ensure positivity. On the other hand, the implicit Euler method is robust but nonlinearly implicit. Instead, we propose to combine the Chang–Cooper method with unconditionally positive Patankar‐type time integration methods, since they are unconditionally positive, robust for stiff problems, only linearly implicit, and also higher‐order accurate. We describe the combined approach, analyze it, and present a relevant numerical example demonstrating advantages compared to schemes proposed in the literature.

An improved Euler method for time fractional nonlinear subdiffusion equations with initial singularity

An improved Euler method for time fractional nonlinear subdiffusion equations with initial singularity

A Non-Standard Finite Difference Discretization Scheme Applied to a Malaria Model

In this research work, a dynamically consistent non-standard finite difference (NSFD) scheme is developed to solve a continuous-time model of malaria transmission with herbal medicine as control strategy. We compared results from NSFD scheme with the standard finite difference methods (4th order Runge-kutta and forward Euler methods). The numerical investigation showed that the proposed NSFD method remains consistent, preserves the positivity of solutions and converges to true equilibrium points of the continuous model independent of the step size h.

A mathematical model to study the role of dystrophin protein in tumor micro-environment.

In this research work, the authors have developed a mathematical model to examine the interaction between dystrophin protein and tumor. The authors formulated a system of ordinary differential equations to describe the dynamics of the dystrophin-tumor interaction system. Jacobian matrix and Routh-Hurwitz stability techniques were used to determine equilibrium points, perform stability and bifurcation analysis, and establish the conditions required for the stability of the proposed model. Numerical simulations are performed using Euler's method to investigate the temporal evolution of the proposed model under different parameter values, such as tumor growth rate and feedback strength of dystrophin protein. The numerical results are presented in tables, and corresponding to each table, a graphical analysis is done. The graphical analysis includes creating phase portraits to visually represent stability regions around the equilibrium points, bifurcation diagrams to identify critical points, and time series analysis to highlight the behavior of the proposed model. The authors explore how variations in dystrophin expression impact tumor progression, identifying potential therapeutic implications of maintaining higher dystrophin levels. This comprehensive analysis enhances our understanding of the dystrophin-tumor interaction, providing a basis for further experimental validation and potential therapeutic strategies.

Modified Shooting with Discretization Validation for Non-Standard Optimal Control Problem: Case Study for Four-Stage Royalty Payment Problem

A modified shooting method augmented by discretization validation is presented in this paper to address the challenges inherent in non-standard optimal control problems. Specifically, a specific case study involving four-stage royalty payment functions is focused on, with the aim of effectively optimizing these complex, non-differentiable functions. The shooting method is adapted and enhanced to compute optimal solutions, and its accuracy is rigorously confirmed through discretization techniques. The primary objectives involve maximizing the performance index and determining optimal control strategies for scenarios where the final state variable remains unknown. In this study, the royalty payment is defined as a four-stage piecewise function. The incorporation of piecewise royalty functions introduces non-differentiability at specific time intervals, necessitating innovative approaches for finding optimal solutions. This, in turn, leads to the utilization of the continuous hyperbolic tangent (tanh) function to address the non-differentiability issue. A hybrid shooting method, combining the Newton and Golden Section Search methods, is employed in the C++ programming language to compute the unknown final state value. A new natural boundary condition, based on established theory, is introduced to further facilitate the investigation. Discretization methods such as Euler, Runge-Kutta, Trapezoidal, and Hermite-Simpson approximations are employed for validation. The validation process entails the use of the AMPL programming language with the MINOS solver. Comparative analyses reveal that the modified shooting method yields more accurate optimal results than discretization methods, thus demonstrating the method’s effectiveness in addressing non-standard optimal control problems. The significance of fundamental theories in addressing real-world challenges is underscored by this research, providing valuable insights for future researchers exploring mathematical approaches in similar contexts. The study contributes to the continued relevance of the academic field, particularly in science and mathematics education.

A high-order physical-constraints-preserving arbitrary Lagrangian–Eulerian discontinuous Galerkin scheme for one-dimensional compressible multi-material flows

In this paper, a high-order physical-constraints-preserving arbitrary Lagrangian–Eulerian (ALE) discontinuous Galerkin scheme is proposed for one-dimensional compressible multi-material flows. Our scheme couples a conservative equation related to the volume-fraction model with the Euler equations for describing the dynamics of fluid mixture. The mesh velocity in the ALE framework is obtained by using an adaptive mesh method that can automatically concentrate the mesh nodes near the regions with large gradient values and greatly reduce the numerical dissipation near material interfaces. Using this adaptive mesh, the resolution of solution near some special regions such as material interfaces can be improved effectively by our scheme. With the appropriate time step condition and using a bound-preserving and positivity-preserving limiter, our scheme can ensure the positivity of density and pressure and the boundness of volume-fraction, which further ensures the computational robustness and degree of confidence of simulations under large density or pressure ratios and so on. In general, our scheme can be applied to the simulations of compressible multi-material flows efficiently with the essentially non-oscillatory property and physical-constraints-preserving (bound-preserving and positivity-preserving) property, and its steps are more concise compared to some other methods such as the indirect ALE methods. Some examples are tested to demonstrate the accuracy, essentially non-oscillatory property and physical-constraints-preserving property of our scheme.

Structure-preserving methods for Marcus stochastic Hamiltonian systems with additive Lévy noise

A general structure-preserving method is proposed for a class of Marcus stochastic Hamiltonian systems driven by additive Lévy noise. The convergence of the symplectic Euler scheme for this systems is investigated by Generalized Milstein Theorem. Realizable numerical implementation of this scheme is also provided in details. Numerical experiments are presented to illustrate the effectiveness and superiority of the proposed scheme. Applications of the method to solve two mathematical physical problems are provided.

A New Multiphysics Finite Element Method for a Quasi-Static Poroelasticity Model

In this paper, we propose a new multiphysics finite element method for a quasi-static poroelasticity model. Firstly, to overcome the displacement locking phenomenon and pressure oscillation, we reformulate the original model into a fluid-fluid coupling problem by introducing new variables-the generalized nonlocal Stokes equations and a diffusion equation, which is a completely new model. Then, we design a fully discrete multiphysics finite element method for the reformulated model-linear finite element pairs for the spatial variables (u,ξ,η) and backward Euler method for time discretization. And we prove that the proposed method is stable without any stabilized term and robust for many parameters and it has the optimal convergence order. Finally, we show some numerical tests to verify the theoretical results.

Dynamics and control of a high-altitude balloon with slung load

Abstract The high-altitude balloon proposed in this paper is a long-life balloon carrying a payload through a cable that flies at 20km altitude in near space. A dynamic model of the system, including the thermodynamics of the buoyancy body coupled with a hanging model of the pod, is developed using the Newton–Euler method. The buoyancy body consists of a helium balloon and a ballonet. A differential pressure difference-based altitude adjustment is achieved by tracking the pressure difference at the target altitude. A dynamic simulation of the buoyancy body with a slung pod in autonomous vertical takeoff and altitude regulation processes is presented. The internal thermodynamic variations and pressure differential of the buoyancy body are given. The air mass exchange and blower flow control of the ballonet are validated. The altitude holding error is analysed. The maximum pull force that the cable can withstand is calculated, and the maximum attitude angles of the pod during the ascending and descending processes are depicted. Simulation results provide basic knowledge for the structural design and payload installation of pods.

An elasto-visco-plastic constitutive model for snow: Theory and finite element implementation

Snow exhibits unique mechanical behaviour due to its evolving properties influenced by temperature, stress conditions, and viscous effects. This paper introduces a nonlinear constitutive model for snow, featuring new formulations for the yield function and strain rate potential, and incorporating viscosity, sintering, and degradation effects. The model is numerically integrated into the Abaqus/Standard FEM software using a fully implicit backward Euler method for time integration and Powell’s hybrid method for linearizing the nonlinear system of equations. The robustness and stability of the numerical scheme ensure accurate simulation of snow behaviour under various loading conditions. The model is finally validated against experimental data available in the literature, demonstrating its effectiveness and reliability in capturing the complex mechanical response of snow.

Option pricing under stochastic volatility on a quantum computer

We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.

First Derivative Approximations and Applications

In this paper, we consider constructions of first derivative approximations using the generating function. The weights of the approximations contain the powers of a parameter whose modulus is less than one. The values of the initial weights are determined, and the convergence and order of the approximations are proved. The paper discusses applications of approximations of the first derivative for the numerical solution of ordinary and partial differential equations and proposes an algorithm for fast computation of the numerical solution. Proofs of the convergence and accuracy of the numerical solutions are presented and the performance of the numerical methods considered is compared with the Euler method. The main goal of constructing approximations for integer-order derivatives of this type is their application in deriving high-order approximations for fractional derivatives, whose weights have specific properties. The paper proposes the construction of an approximation for the fractional derivative and its application for numerically solving fractional differential equations. The theoretical results for the accuracy and order of the numerical methods are confirmed by the experimental results presented in the paper.

Optimal error bounds for the two-point flux approximation finite volume scheme

We consider a finite volume scheme with two-point flux approximation (TPFA) to approximate a Laplace problem when the solution exhibits no more regularity than belonging to H 0 1 ( Ω ) H^1_0(\Omega ) . We define an error between the approximate solution and the exact one, involving their difference and the difference of normal gradients to the faces of the mesh. We prove that this error behaves as the sum of an interpolation error and a conformity error. As a consequence, some error bounds, depending on the regularity of the continuous solution, can be obtained for both the approximate solution and an approximate gradient post-processed from this approximate solution. A numerical example illustrates the error estimate in the context of a solution with minimal regularity. This result is extended to evolution problems discretized via the implicit Euler scheme in an appendix.

[formula omitted]-Wasserstein contraction for Euler schemes of elliptic diffusions and interacting particle systems

We show L2-Wasserstein contraction for the transition kernel of a discretised diffusion process, under a contractivity at infinity condition on the drift and a sufficiently high diffusivity requirement. This extends recent results that, under similar assumptions on the drift but without the diffusivity restrictions, showed L1-Wasserstein contraction, or Lp-Wasserstein bounds for p>1 that were, however, not true contractions. We explain how showing a true L2-Wasserstein contraction is crucial for obtaining a local Poincaré inequality for the transition kernel of the Euler scheme of a diffusion. Moreover, we discuss other consequences of our contraction results, such as concentration inequalities and convergence rates in KL-divergence and total variation. We also study corresponding L2-Wasserstein contraction for discretisations of interacting diffusions. As a particular application, this allows us to analyse the behaviour of particle systems that can be used to approximate a class of McKean-Vlasov SDEs that were recently studied in the mean-field optimisation literature.

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Investigating the impact of stochasticity on HIV infection dynamics in CD4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{+}$$\\end{document} T cells using a reaction-diffusion model

The disease dynamics affect the human life. When one person is affected with a disease and if it is not treated well, it can weaken the immune system of the body. Human Immunodeficiency Virus (HIV) is a virus that attacks the immune system, of the body which is the defense line against diseases. If it is not treated well then HIV progresses to its advanced stages and it is known as Acquired Immunodeficiency Syndrome (AIDS). HIV is typically a disease that can transferred from one person to another in several ways such as through blood, breastfeeding, sharing needles or syringes, and many others. So, the need of the hour is to consider such important disease dynamics and that will help mankind to save them from such severe disease. For the said purpose the reaction-diffusion HIV CD4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{+}$$\\end{document} T cell model with drug therapy under the stochastic environment is considered. The underlying model is numerically investigated with two time-efficient schemes and the effects of various parameters used in the model are analyzed and explained in a real-life scenario. Additionally, the obtained results will help the decision-makers to avoid such diseases. The random version of the HIV model is numerically investigated under the influence of time noise in Ito^\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\hat{o}}$$\\end{document} sense. The proposed stochastic backward Euler (SBE) scheme and proposed stochastic Implicit finite difference (SIFD) scheme are developed for the computational study of the underlying model. The consistency of the schemes is proven in the mean square sense and the given system of equations is compatible with both schemes. The stability analysis proves that both schemes and schemes are unconditionally stable. The given system of equations has two equilibria, one is disease-free equilibrium (DFE) and the other is endemic equilibrium. The simulations are drawn for the different values of the parameters. The proposed SBE scheme showed the convergent behavior towards the equilibria for the given values of the parameters but also showed negative behavior that is not biological. The proposed SIFD scheme showed better results as compared with the stochastic SBE scheme. This scheme has convergent and positive behavior towards the equilibria points for the given values of the parameters. The effect of various parameters is also analyzed. Simulations are drawn to evaluate the efficacy of the schemes.

Performance Analysis and Parallel Scalability of Numerical Methods for Fractional-in-Space Diffusion Problems with Adaptive Time Stepping

We study numerical methods and algorithms for time-dependent fractional-in-space diffusion problems. The considered anomalous diffusion is modelled by the fractional Laplacian (−Δ)α, 0<α<1, following the integral definition. Fractional diffusion is non-local, and the finite element method (FEM) discretization in space leads to a dense stiffness matrix. It is well known that numerically solving such non-local boundary value problems is expensive. Difficulties increase significantly when the problem is time-dependent. The aim of the article is to develop computationally efficient methods and algorithms. There are two main features of our approach. Hierarchical semi-separable (HSS) compression is applied for an approximate solution of the arising linear systems. For time discretization, we use an adaptive forward–backward Euler scheme. The properties of the composite algorithm thus obtained are investigated. In particular, the block representation of HSS compression allowed us to upgrade the HSS solver to efficiently handle varying diagonally perturbed transition matrices corresponding to changing time steps. The contribution of the paper is threefold. The methods are completely constructive, which allows for a clearly structured description of the algorithms. A theoretical estimate of the computational complexity is presented. It shows the advantages of the adaptive time stepping in combination with the HSS solver. Theoretical results are supported by representative numerical experiments. Both sequential and parallel scalability and efficiency are analyzed. The presented results provide convincing proof of the concept of the proposed methods and algorithms.

Penerapan Metode Runge-Kutta Orde 3 Untuk Penyelesaian Persamaan Diferensial Biasa

This research studies the application of the nth order Runge-kutta method as a numerical solution to ordinary differential equations. This method was chosen because it is able to provide high accuracy and flexibility in various PDB problems. We implement the nth-order Runge-Kutta algorithm in MATLAB and compare with other numerical methods, such as Euler's method. The results show that the nth order Runge-Kutta method is able to produce more accurate solutions, especially for nonlinear systems. This research makes a significant contribution to the development of numerical solutions for PDB and shows the potential of MATLAB as an effective tool for numerical simulation. Sensitivity analyzes of parameters and time steps were also performed to understand the impact of variations on stability and convergence.

The energy-diminishing weak Galerkin finite element method for the computation of ground state and excited states in Bose-Einstein condensates

In this paper, we employ the weak Galerkin (WG) finite element method and the imaginary time method to compute both the ground state and the excited states in Bose-Einstein condensate (BEC) which is governed by the Gross-Pitaevskii equation (GPE). First, we use the imaginary time method for GPE to get the nonlinear parabolic partial differential equation. Subsequently, we apply the WG method to spatially discretize the parabolic equation. This yields a semi-discrete scheme, in which an energy function is explicitly defined. For the case β⩾0, we demonstrate that the energy is diminishing with respect to time t at each time step. Applying the backward Euler scheme for temporal discretization yields a fully discrete scheme. For the case β=0, we provide a mathematical justification, establishing the convergence analysis for the numerical solution of the ground state. Moreover, based on the theory of solving eigenvalue problems using the WG method, we present the error estimates between the ground state and its numerical solution under the H1 and L2 norms. Numerical experiments are provided to illustrate the effectiveness of the proposed schemes. Moreover, the results indicate that our method also can compute the first excited state, achieving optimal convergence orders.

Unsteady numerical simulations of aircraft icing based on the dynamic grid method

In this study, an unsteady numerical simulation method based on dynamic grids was established to solve unsteady aircraft icing processes. The dual-time step method was used to achieve the time advancement of the transient airflow and droplets fields. The droplets trajectory was calculated using the Euler method, which incorporates a supercooled large droplets model. The transient evolution of the water film and ice layer on the substrate surface was considered in the icing thermodynamic model. The accuracy of the unsteady method for ice-shape prediction was verified via comparison and analysis of numerical examples. Additionally, the rime and glaze ice formation processes were explored, and the coupling effects of ice horn growth and the air convective heat transfer coefficient, water collection coefficient, and water film flow on the substrate surface were studied. The ice shapes calculated by unsteady and quasi-steady methods were compared. The results indicated that the latter predicts a larger icing range than the former. Moreover, there may be more obvious distinctions between these two methods in predicting glaze ice.