Explicit Splitting Research Articles

Partially explicit splitting scheme with explicit–implicit-null method for nonlinear multiscale flow problems

In this work, we present an efficient approach to solve nonlinear high-contrast multiscale diffusion problems. We incorporate the explicit–implicit-null (EIN) method to separate the nonlinear term into a linear term and a damping term, and then utilize the implicit and explicit time marching scheme for the two parts respectively. Due to the multiscale property of the linear part, we further introduce a temporal partially explicit splitting scheme and construct suitable multiscale subspaces to speed up the computation. The approximated solution is split into these subspaces associated with different physics. The temporal splitting scheme employs implicit discretization in the subspace with small dimension that representing the high-contrast property and uses explicit discretization for the other subspace. We exploit the stability of the proposed scheme and give the condition for the choice of the linear diffusion coefficient. The convergence of the proposed scheme is provided. Several numerical tests are performed to show the efficiency and accuracy of the proposed scheme.

Lost in the Forest: Encoding categorical variables and the absent levels problem

Levels of a predictor variable that are absent when a classification tree is grown can not be subject to an explicit splitting rule. This is an issue if these absent levels are present in a new observation for prediction. To date, there remains no satisfactory solution for absent levels in random forest models. Unlike missing data, absent levels are fully observed and known. Ordinal encoding of predictors allows absent levels to be integrated and used for prediction. Using a case study on source attribution of Campylobacter species using whole genome sequencing (WGS) data as predictors, we examine how target-agnostic versus target-based encoding of predictor variables with absent levels affects the accuracy of random forest models. We show that a target-based encoding approach using class probabilities, with absent levels designated the highest rank, is systematically biased, and that this bias is resolved by encoding absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordinal encoding predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are encoded according to their similarity to each of the other levels in the training data. We show that the PCO-encoding method performs at least as well as the target-based approach and is not biased.

Partially explicit splitting method for a multi-physics problem

Multi-physics are widely studied and used in numerical analysis and simulation. Physics-based splitting methods are introduced, in which different methods are applied to solve each individual aspect of a physical system in multiple steps. The implicit methods are typically used for temporal discretization and are unconditionally stable such that the time step size can be large during the computation. However, the implicit methods are more complicated to solve, especially for nonlinear problems. In contrast, the explicit methods are relatively easier to compute, while they may require a much smaller time step size. In this paper, we propose a partially explicit scheme with physics-based splitting. We take the convection diffusion equation as an example. In this scheme, the convection equation is solved using the exact solution, and then we solve the diffusion equation via finite element methods. Some degrees of freedom in the diffusion equation are treated implicitly while others are treated explicitly. We can change the large-scale implicit system into one smaller-scale implicit system and one smaller-scale explicit system, which provides saving in computation. We analyze the stability and the order of accuracy. We also present some numerical results to show the performance of our proposed scheme.

An adaptive space and time method in partially explicit splitting scheme for multiscale flow problems

We propose a space and time adaptive framework for a partially explicit splitting scheme of flow problems in multiscale media. We consider the parabolic equation with high-contrast multiscale coefficients. It is known that sufficient number of multiscale basis are needed to capture non-local features and information across all scales. Moreover, the time step size in an explicit discretization of the problem is affected by the magnitude of the coefficient. To improve computational efficiency, in this work, we introduce an adaptive algorithm to refine temporal size and enrich multiscale spaces. We first utilize a stable temporal splitting scheme with an appropriate construction of multiscale spaces to ensure that the time step size is independent of the contrast. The multiscale subspaces are then constructed to handle the fast-flow and slow-flow parts separately, and implicit and explicit discretization is adopted for the degrees of freedom with respect to the two multiscale subspaces, respectively. A multirate time stepping is introduced for the two parts considering that the flow rates vary significantly in different regions due to the high-contrast features of the multiscale coefficients. We derive both temporal and spatial error estimators to identify local regions for two components of the solutions. Start with coarser time step sizes and a few important multiscale basis functions in each subspace, we can adaptively refine time stepping and add additional basis with constraint energy minimizing properties based on the derived error indicators. The stability and convergence of the method are studied, and several numerical tests are presented to demonstrate the performance of the proposed method.

A novel class of explicit energy-preserving splitting methods for charged-particle dynamics

In this letter, based on the exponential scalar auxiliary variable technology, we propose and study a new class of explicit energy-preserving splitting methods for solving the charged-particle dynamics. The energy-preserving property of these methods is rigorously analysed. We also provide the error estimates for the new methods. Numerical computations are presented, which confirm the effectiveness and superiority of these novel methods in comparison with the standard scalar auxiliary variable approach.

The massless non-adjacent double off-shell scalar box integral — branch cut structure and all-order epsilon expansion

We generalize the result of our recent paper on the massless single off-shell scalar box integral to the case of two non-adjacent end points off the light cone. An analytic result in d = 4 − 2ε dimensions is established in terms of four Gauss hypergeometric 2F1 functions respectively their single-valued counterparts. This allows for an explicit splitting of real and imaginary parts, as well as an all-order ε-expansion in terms of single-valued polylogarithms.

STRUCTURE PRESERVING SPLITTING TECHNIQUES FOR EBOLA REACTION–DIFFUSION EPIDEMIC SYSTEM

In this paper, we deal with the numerical solution of the reaction–diffusion Ebola epidemic model. The diffusion which is an important phenomenon for the epidemic model is included in the model. This inclusion has made the model more comprehensive for studying the disease dynamics in the human population. The quantities linked with the model indicate the population sizes which are taken as absolute, therefore, the numerical schemes utilized to solve the underlying Ebola epidemic system should sustain the positivity. The numerical approaches used to solve the underlying epidemic models are explicit nonstandard finite difference operator splitting (ENSFD-OS) and implicit nonstandard finite difference operator splitting (INSFD-OS) techniques. These schemes preserve all the physical features of the state variables, i.e. projected schemes hold the positive solution acquired by the Ebola diffusive epidemic model. The underlying epidemic model illustrates two stable steady states, a virus-free state, and a virus existence state. The suggested approaches retain the stability of each of the steady states possessed by the assumed epidemic model. A numerical example and simulations for validation of all the characteristics of suggested techniques are also investigated.

Modelling and Simulation of Transverse Homogeneity of Slotted Coating Process

Slot coating is a kind of precise coating process, which is widely used in the production of membrane materials. The thickness deviation of coating along the transverse direction is the key index to measure the coating quality. The homogeneity of the transverse distribution of the coating velocity at the exit of the die is the most direct factor leading to the transverse thickness deviation of the coating. To predict the transverse homogeneity of slot coating, a mathematical model for the internal flow field in the die was established, where the incompressible Navier-Stokes equations were used to describe the fluid flow and generalized Newtonian fluid model was used to describe the rheological behaviour of non-Newtonian coating. An efficient explicit splitting algorithm based on finite element method is proposed to solve the model. To verify the effectiveness of the model and the algorithm, the internal flow field of a trapezoidal double cavity die with equal section was simulated. The transverse homogeneity obtained was close to the experimental measured value, which verified the validity of the model. Furthermore, the influence of measuring slot width on the transverse homogeneity of outlet velocity is studied, and the simulation results are consistent with the theoretical expectations.

A second-order extension of a robust implicit–explicit acoustic-transport splitting scheme for two-phase flows

Diffuse interface methods have proven their ability to simulate complex two-phase flows. A number of robust numerical schemes have been developed to simulate such flows involving large density and pressure ratios. Diffusion induced by these methods, however, makes it difficult to localize the interface between the two fluids. To overcome this issue, while retaining the advantages of diffuse interface methods, a second-order extension using a Monotonic Upstream-centered Scheme for Conservation Laws-type (MUSCL-type) method of the implicit–explicit acoustic-transport splitting scheme introduced in Peluchon et al. (2017) is presented. A specific compressive limiter is used for the volume fraction in order to limit the diffusion of the interface between the two fluids.Numerical simulations are presented to illustrate the capability of the proposed new method to simulate highly complex compressible two-phase flows.

Multirate Partially Explicit Scheme for Multiscale Flow Problems

For time-dependent problems with high-contrast multiscale coefficients, the time step size for explicit methods is affected by the magnitude of the coefficient parameter. With a suitable construction of multiscale space, one can achieve a stable temporal splitting scheme where the time step size is independent of the contrast. Consider the parabolic equation with heterogeneous diffusion parameter, the flow rates vary significantly in different regions due to the high-contrast features of the diffusivity. In this work, we aim to introduce a multirate partially explicit splitting scheme to achieve efficient simulation with the desired accuracy. We first design multiscale subspaces to handle flow with different speed. For the fast flow, we obtain a low-dimensional subspace with respect to the high-diffusive component and adopt an implicit time discretization scheme. The other multiscale subspace will take care of the slow flow, and the corresponding degrees of freedom are treated explicitly. Then a multirate time stepping is introduced for the two parts. The stability of the multirate methods is analyzed for the partially explicit scheme. Moreover, we derive local error estimators corresponding to the two components of the solutions and provide an upper bound of the errors. An adaptive local temporal refinement framework is then proposed to achieve higher computational efficiency. Several numerical tests are presented to demonstrate the performance of the proposed method.

A splitting method for SDEs with locally Lipschitz drift: Illustration on the FitzHugh-Nagumo model

In this article, we construct and analyse an explicit numerical splitting method for a class of semi-linear stochastic differential equations (SDEs) with additive noise, where the drift is allowed to grow polynomially and satisfies a global one-sided Lipschitz condition. The method is proved to be mean-square convergent of order 1 and to preserve important structural properties of the SDE. First, it is hypoelliptic in every iteration step. Second, it is geometrically ergodic and has an asymptotically bounded second moment. Third, it preserves oscillatory dynamics, such as amplitudes, frequencies and phases of oscillations, even for large time steps. Our results are illustrated on the stochastic FitzHugh-Nagumo model and compared with known mean-square convergent tamed/truncated variants of the Euler-Maruyama method. The capability of the proposed splitting method to preserve the aforementioned properties may make it applicable within different statistical inference procedures. In contrast, known Euler-Maruyama type methods commonly fail in preserving such properties, yielding ill-conditioned likelihood-based estimation tools or computationally infeasible simulation-based inference algorithms.

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning

Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning reconstruction using a hybrid linear and non-linear methodology. In rRAKI, non-linear CNNs are trained jointly with a linear convolution implemented via a skip connection. In effect, the linear part provides a baseline reconstruction, while the non-linear CNN that runs in parallel provides further reduction of artifacts and noise arising from the linear part. The explicit split between the linear and non-linear aspects of the reconstruction also help improve interpretability compared to purely non-linear methods. Experiments were conducted on the publicly available fastMRI datasets, as well as high-resolution anatomical imaging, comparing GRAPPA and its variants, compressed sensing, RAKI, Scan Specific Artifact Reduction in K-space (SPARK) and the proposed rRAKI. Additionally, highly-accelerated simultaneous multi-slice (SMS) functional MRI reconstructions were also performed, where the proposed rRAKI was compred to Read-out SENSE-GRAPPA and RAKI. Our results show that the proposed rRAKI method substantially improves the image quality compared to conventional parallel imaging, and offers sharper images compared to SPARK and ℓ1-SPIRiT. Furthermore, rRAKI shows improved preservation of time-varying dynamics compared to both parallel imaging and RAKI in highly-accelerated SMS fMRI.

Numerical approximation to semi-linear stiff neutral equations via implicit–explicit general linear methods

Among the initial value problems of semi-linear neutral equations, there are a class of so-called stiff problems, where the classical explicit methods do not work as the methods have only bounded stability regions, which confine the computational stepsize to be excessively small and thus leads to an unsuccessful calculation. For resolving this difficult issue, ones turned to develop the implicit methods with unbounded stability regions to solve the stiff problems. Nevertheless, it is well-known that the implementation of an implicit method needs a large computational cost. In order to improve the computational efficiency, in Refs. Tan and Zhang (2018, 2020), the authors adopted the implicit–explicit (IMEX) splitting technique to derive the extended IMEX one-leg methods and IMEX Runge–Kutta methods, respectively. Unfortunately, these two methods have the serious order barrier. So far, for stiff neutral equations (SNEs), no IMEX method with order more than two has been found. To improve the computational accuracy and efficiency of IMEX methods, in the present paper, we construct a class of extended implicit–explicit general linear (EIEGL) methods for solving semi-linear SNEs. Under some suitable conditions, an EIEGL method is proved to be stable and convergent of order p whenever the underlying implicit–explicit general linear (IEGL) method has order p and stage order p. With applications to several concrete problems of SNEs, the computational accuracy of EIEGL methods are further illustrated, where we also verify that the convergence order of EIEGL methods can exceed two, namely, third- and fourth-order EIEGL methods can be obtained. Moreover, based on a numerical comparison with the extended implicit general linear (EIGL) methods, the advantage of EIEGL methods in computational efficiency is shown.

Minimizing Overloads of Critical Tasks Using Machine Learning in CPS by Extending Resources

With all the growing variety of solutions plus industries as well as nuclear, substance, aerospace, as well as auto sectors to come down with cyber-physical Systems (CPSs), methods remain now actuality seriously loaded. CPSs includes varied dangerous jobs that stand protection dangerous (1) that is high or perhaps non-protection crucial (2) that is low. For conventional job arranging, nearly almost on the current arranging algorithms offer terrible functionality for high criticality jobs, if the method suffers from overburden as well as doesn't present explicit splitting up with various criticality duties to make the most of utilizing cloud online resources. Below, a framework is proposed by us to plan the mixed criticality duties by examining the deadlines of theirs as well as delivery occasions that use the overall presentation of similar handling done by OpenMP (Open Multi-Processing). The suggested agenda presents a piece of ML-based estimate for a job unloading within the area of cloud. Furthermore, it clarifies to perform the nominated variety of low dangerous things within the area of cloud even though the extraordinary serious jobs are operated over the regional CPUs over the method clog. Consequently, the high criticality jobs fulfil almost all the deadlines of theirs and also the method accomplishes a tremendous enhancement within the general delivery period as well as much better throughput. Additionally, the investigational outcomes using OpenMP present the usefulness of utilizing the subdivided arranging during a worldwide arranging technique upon multiprocessor methods to accomplish the works isolation.

Research on Splitting Isomorphism of Leibniz Algebra and Non-Abelian expansion

In this study, Leibniz algebras and the derivations and properties of Leibniz algebras were given, respectively. The stable automorphism group of explicit splitting extension was calculated via the stable automorphism group of Abelian extension of finite group splitting. Based on the stable automorphism group of the splitting extension studied, the non-Abelian extension and the second order non-Abelian co-homology group of Leibniz algebra were investigated in detail according to the stable automorphism group of the splitting extension.

A fast and efficient numerical algorithm for Swift–Hohenberg equation with a nonlocal nonlinearity

A high-order accurate fast explicit operator splitting scheme for the Swift–Hohenberg equation with a nonlocal nonlinearity is presented in this paper. We discretize the Swift–Hohenberg equation by a Fourier spectral method in space and an operator splitting scheme with second-order accurate in time, respectively. The second-order strong stability preserving Runge–Kutta (SSP-RK) method is presented to deal with the nonlinear part. The numerical simulations including the convergence and stability test of the proposed scheme are performed to demonstrate the efficiency of our proposed method.

Stability and Error Estimate of the Operator Splitting Method for the Phase Field Crystal Equation

In this paper, we propose a second-order fast explicit operator splitting method for the phase field crystal equation. The basic idea lied in our method is to split the original problem into linear and nonlinear parts. The linear subproblem is numerically solved using the Fourier spectral method, which is based on the exact solution and thus has no stability restriction on the time-step size. The nonlinear one is solved via second-order strong stability preserving Runge–Kutta method. The stability and convergence are discussed in $$L^2$$ -norm. Numerical experiments are performed to validate the accuracy and efficiency of the proposed method. Moreover, energy degradation and mass conservation are also verified.

Industrial Innovation, Labour Productivity, Sales and Employment

This article examines the relationship between firm-level innovation and employment growth for industrial firms in the Netherlands. The empirical analysis uses four waves of the CIS survey for the period 2002-2010. It extends the literature by making an explicit split between the expansion effect of innovation and the labour productivity effect. The results show that both product and process innovation increase labour productivity and therefore induce direct reductions in employment. However, these negative employment effects are more than compensated by increases in sales, implying that both process and product innovations increase employment. In this article for the first time the relationship between both product and process innovations and employment is decomposed in a systematic manner based on explicit econometric equations on the relationship between innovation and labour productivity respectively sales. It is argued that the effects for sales and labour productivity are probably underestimated in all research that uses CIS survey data because these do not show the price effects of increased productivity, but that this effect cancels out in the estimated employment equation.

REBOUNDx: a library for adding conservative and dissipative forces to otherwise symplectic N-body integrations

ABSTRACT Symplectic methods, in particular the Wisdom–Holman map, have revolutionized our ability to model the long-term, conservative dynamics of planetary systems. However, many astrophysically important effects are dissipative. The consequences of incorporating such forces into otherwise symplectic schemes are not always clear. We show that moving to a general framework of non-commutative operators (dissipative or not) clarifies many of these questions, and that several important properties of symplectic schemes carry over to the general case. In particular, we show that explicit splitting schemes generically exploit symmetries in the applied external forces, which often strongly suppress integration errors. Furthermore, we demonstrate that so-called ‘symplectic correctors’ (which reduce energy errors by orders of magnitude at fixed computational cost) apply equally well to weakly dissipative systems and can thus be more generally thought of as ‘weak splitting correctors’. Finally, we show that previously advocated approaches of incorporating additional forces into symplectic methods work well for dissipative forces, but give qualitatively wrong answers for conservative but velocity-dependent forces like post-Newtonian corrections. We release REBOUNDx, an open-source C library for incorporating additional effects into REBOUNDN-body integrations, together with a convenient python wrapper. All effects are machine independent and we provide a binary format that interfaces with the SimulationArchive class in REBOUND to enable the sharing and reproducibility of results. Users can add effects from a list of pre-implemented astrophysical forces, or contribute new ones.

Error Analysis and Numerical Simulations of Strang Splitting Method for Space Fractional Nonlinear Schrödinger Equation

In this paper, we study a fast explicit operator splitting method for space fractional nonlinear Schrodinger equation in one (1D), two (2D) and three dimensions (3D) with periodic boundary conditions. The equation is split into linear and nonlinear parts: the linear part is solved by the Fourier spectral method, which is based on the exact solution and thus has no stability restriction on the time-step size; the nonlinear subequation is then solved analytically due to the availability of a closed-form solution. The rigorous analysis of the discrete mass conservation principle and the convergence rate of the proposed algorithm are proved. The theoretical results show the proposed method is $$L^2$$ unconditionally stable, second order accurate in time, whereas the spatial accuracy depends on the regularity of the solution. Numerical experiments for 1D, 2D and 3D cases demonstrate the efficiency of the proposed method.