The random timestep Euler method and its continuous dynamics

The voluntary prisoner’s dilemma (VPD) game has sparked interest from various fields since it was proposed as an effective mechanism to incentivize cooperative behavior. Current studies show that the inherent cyclic dominance of the strategies of the VPD game results in periodic oscillations in population. This paper investigated the influence of the level of individual rationality and the size of a population on the evolutionary dynamics of the VPD game. Different deterministic dynamics, such as the replicator dynamic, the Smith dynamic, the Brown-von Neumann-Nash (BNN) dynamic and the best response (BR) dynamic, for the evolutionary VPD game were modeled and simulated. The stochastic evolutionary dynamics based on quasi birth and death (QBD) process was proposed for the evolutionary VPD game and compared with deterministic dynamics. The results indicated that with the increase of the loners’ fixed payoff, the loner is more likely to remain in the stable state of a VPD game under any of the dynamics mentioned above. However, the different speeds of motion under the dynamics in the cycle dominance proved to be diverse under different evolutionary dynamics and also highly sensitive to the rationality of individuals in a population. Furthermore, in QBD stochastic dynamics, the size of the population has a remarkable effect on the possibility distribution. When the population size increases, the limited distribution of the QBD process will be in accordance with the results in the deterministic dynamics.

A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomize ODE solvers by adding a random forcing term, we show that a probability measure over the numerical solution of ODEs can be obtained by introducing suitable random time-steps in a classical time integrator. This intrinsic randomization allows for the conservation of geometric properties of the underlying deterministic integrator such as mass conservation, symplecticity or conservation of first integrals. Weak and mean-square convergence analysis are derived. We also analyse the convergence of the Monte Carlo estimator for the proposed random time step method and show that the measure obtained with repeated sampling converges in the mean-square sense independently of the number of samples. Numerical examples including chaotic Hamiltonian systems, chemical reactions and Bayesian inferential problems illustrate the accuracy, robustness and versatility of our probabilistic numerical method.

We have studied whether living things investigated under the same measuring conditions can generate signals with different types of dynamics. We also wanted to detect the possible effects of low-intensity microwaves using parameters of deterministic chaos. For this purpose, two sets of electroretinograms were analysed by methods aimed at recognizing different types of dynamics. Both sets included the time series recorded from objects exposed to low-intensity microwaves and those that were not exposed. The analytical methods are based on nonlinear forecasting and a “surrogate data” technique. Although the experimental conditions were identical for the two sets, we have shown that both have time series with deterministic and stochastic dynamics. We also found that the use of parameters of deterministic dynamics is insufficient to distinguish between the sets.

Periodic Solutions in the Golden Sixties: The Birth of a Continuation Theorem

Introduction Suggestions for the readerSuggestions for the reader Fredholm mappings of index zero and linear boundary value problems Degree theory for some classes of mappings Duality theorems for several fixed point operators associated to periodic problems for ordinary differential equations Existence theorems for equations in normed spaces Boundary value problems for second order nonlinear vector differential equations Periodic solutions of ordinary differential equations with one-sided growth restrictions Bound sets for functional differential equations The index of isolated zeros of some mappings Bifurcation theory Periodic solutions of autonomous ordinary differential equations around an equilibrium References.

The Green’s function is widely used in solving boundary value problems for differential equations, to which many mathematical and physical problems are reduced. In particular, solutions of partial differential equations by the Fourier method are reduced to boundary value problems for ordinary differential equations. Using the Green's function for a homogeneous problem, one can calculate the solution of an inhomogeneous differential equation. Knowing the Green's function makes it possible to solve a whole class of problems of finding eigenvalues in quantum field theory. The developed method for constructing the Green’s function of boundary value problems for ordinary linear differential equations is described. An algorithm and program for calculating the Green's function of boundary value problems for differential equations of the second and third orders in an explicit analytical form are presented. Examples of computing the Green's function for specific boundary value problems are given. The fundamental system of solutions of ordinary differential equations with singular points needed to construct the Green's function is calculated in the form of generalized power series with the help of the developed programs in the Maple environment. An algorithm is developed for constructing the Green's function in the form of power series for second-order and third-order differential equations with given boundary conditions. Compiled work programs in the Maple environment for calculating the Green functions of arbitrary boundary value problems for differential equations of the second and third orders. Calculations of the Green's function for specific third-order boundary value problems using the developed program are presented. The obtained approximate Green’s function is compared with the known expressions of the exact Green’s function and very good agreement is found

First, we prove a necessary and sufficient condition for global in time existence of all solutions of an ordinary differential equation (ODE). It is a condition of one‐sided estimate type that is formulated in terms of so‐called proper functions on extended phase space. A generalization of this idea to stochastic differential equations (SDE) and parabolic equations (PE) allows us to prove similar necessary and sufficient conditions for global in time existence of solutions of special sorts: L1‐complete solutions of SDE (this means that they belong to a certain functional space of L1 type) and the so‐called complete Feller evolution families giving solutions of PE. The general case of equations on noncompact smooth manifolds is under consideration.

Dedicated to Professor Yuzan He on the Occasion of his 80th BirthdayIn this paper, we employ the complex method to obtain all meromorphic solutions of an auxiliary ordinary differential equation at first and then find out all meromorphic exact solutions of the combined KdV–mKdV equation and variant Boussinesq equations. Our result shows that all rational and simply periodic exact solutions of the combined KdV–mKdV equation and variant Boussinesq equations are solitary wave solutions, the method is more simple than other methods, and there exist some rational solutions wr,2(z) and simply periodic solutions ws,2(z) that are not only new but also not degenerated successively by the elliptic function solutions. Copyright © 2016 John Wiley & Sons, Ltd.

In modern engineering researches, dynamics of systems are often studied by well-design computer simulations, and these dynamics are usually depicted in the form of vector-or matrix-valued ordinary differential equation (ODE) systems. A common approach for solution of matrix-valued ODE in MATLAB is to use the Kronecker product and vectorization technique. However, the vectorization procedure may be troublesome, and it can be difficult if not impossible to vectorize a complicated matrix-valued ODE (especially when it is nonlinear). Differing from the traditional approach, this paper proposes an elegant, compact and effective tool (named MatrixODE) for solving a matrix-valued ODE with arbitrary right hand side. As a complement and extension of the built-in solvers in MATLAB, the proposed MatrixODE tool allows solving a matrix-valued ODE using matrix language directly, i.e., users do not need to perform matrix-vector transformations. Application of the proposed MatrixODE tool to online time-varying matrix inversion further substantiates its effectiveness for matrix-valued ODE solving.

Solving ordinary differential equations (ODEs) is vital in diverse fields. However, it is difficult to obtain the exact analytical solutions of ODEs due to their changeable mathematical forms. Traditional numerical methods can find approximate solutions for specific ODEs. Unfortunately, they often suffer from ODEs' forms and characteristics. To approximate different types of ODEs, this paper proposes a generic method based on adaptive differential evolution. Besides, in order to further reduce the error of the obtained approximate solutions, an improved Fourier periodic expansion function is developed, which is then combined with the least square weight method to formulate the ODEs as an optimization problem. Since the proposed method is not limited to ODEs' forms and constraint conditions, it can be used to approximate any ODEs, including linear ODEs and nonlinear ODEs. The proposed method is evaluated on twenty popular test cases. The results indicate that the proposed method is able to accurately approximate different ODEs with better performance compared with other methods.

Probabilistic ordinary differential equation (ODE) solvers have been introduced over the past decade as uncertainty-aware numerical integrators. They typically proceed by assuming a functional prior to the ODE solution, which is then queried on a grid to form a posterior distribution over the ODE solution. As the queries span the integration interval, the approximate posterior solution then converges to the true deterministic one. Gaussian ODE filters, in particular, have enjoyed a lot of attention due to their computational efficiency, the simplicity of their implementation, and their provable fast convergence rates. In this article, we extend the methodology to stochastic differential equations (SDEs) and propose a probabilistic simulator for SDEs. Our approach involves transforming the SDE into a sequence of random ODEs using piecewise differentiable approximations of the Brownian motion. We then apply probabilistic ODE solvers to the individual ODEs, resulting in a pathwise probabilistic solution to the SDE. We establish worst-case strong 1.5 local and 1.0 global convergence orders for a specific instance of our method. We further show how we can marginalise the Brownian approximations, by incorporating its coefficients as part of the prior ODE model, allowing for computing exact transition densities under our model. Finally, we numerically validate the theoretical findings, showcasing reasonable weak convergence properties in the marginalised version.

Motivated by important geophysical applications we study a non-linear model of geyser dynamics under the influence of external stochastic forcing. It is shown that the deterministic dynamics is substantially dependent on system parameters leading to the following evolutionary scenaria: (i) oscillations near a stable equilibrium and a transient tendency of the phase trajectories to a spiral sink or a stable node (pre-eruption regime), and (ii) fast escape from equilibrium (eruption regime). Even a small noise changes the system dynamics drastically. Namely, a low-intensity noise generates the small amplitude stochastic oscillations in the regions adjoining to the stable equilibrium point. A small buildup of noise intensity throws the system over its separatrix and leads to eruption. The role of the friction coefficient and relative pressure in the deterministic and stochastic dynamics is studied by direct numerical simulations and stochastic sensitivity functions technique.

On the comparison of numerical methods for the integration of kinetic equations in atmospheric chemistry and transport models

Ordinary differential equations (ODEs) are a fundamental tool for modeling dynamical systems in various scientific fields. However, solving ODEs analytically can be challenging, and numerical methods can be computationally expensive or inaccurate. In recent years, deep neural networks (DNNs) have emerged as a promising alternative for solving ODEs. This paper focuses on the utilization of DNNs to solve first and second-order ODEs. Existing works in this field are reviewed, with an emphasis on identifying their limitations. Subsequently, an approach is proposed to overcome these limitations. The proposed method utilizes a combination of a deep neural network and various activation functions to achieve accurate and efficient solutions for ordinary differential equations (ODEs). Deep neural networks (DNNs) have shown significant potential in solving ordinary differential equations (ODEs) due to their ability to approximate complex functions and their capacity to learn from data. Traditional numerical methods for solving ODEs have some limitations such as requiring large amounts of computation, limited accuracy, and difficulty in handling high-dimensional systems. The aim is to overcome these limitations by leveraging the capabilities of DNNs, that have the potential to learn and generalize from data, provide more accurate solutions, requiring fewer computational resources, and being able to handle high-dimensional systems. This paper aims to explore the use of DNNs in solving 1st and 2nd order differential equations, and proposes a novel approach for improving the accuracy and efficiency of the solution process.

TrapezoidalNet: A new network architecture inspired from the numerical solution of ordinary differential equations