Spatial Differential Operator Research Articles

Generalized Finite Integration Method with Laplace transform for European option pricing under Black–Scholes and Heston models

In this paper, we combine a recently developed Generalized Finite Integration Method (GFIM) with Laplace transform technique for pricing options under the Black Scholes model and Heston model respectively. Instead of using traditional time-stepping process, we first perform Laplace transform on the governing equation and boundary conditions to remove the temporal derivatives. The Generalized Finite Integration Method is then exploited to handle the spatial differential operators in the transformed space. From numerical Laplace inversion algorithm, we restore the required time-dependent option price. For verification, several option pricing models governed by one-dimensional Black–Scholes equation and two-dimensional extended Heston equation are constructed to demonstrate the efficiency and feasibility of the proposed approach.

Data-Driven Spatial Adaptive Terminal Iterative Learning Predictive Control for Automatic Stop Control of Subway Train With Actuator Saturation

A data-driven spatial adaptive terminal iterative learning predictive control (SATILPC) scheme with actuator saturation is proposed for automatic stop control of the subway train. Considering the outstanding repetitive operation pattern and the unavailable accurate model of a subway train, the unknown train dynamics is firstly transformed into a nonlinear discrete form in spatial domain via the spatial differential operator. Since the train automatic stop control (TASC) only concentrates on the tracking performances of the terminal position and terminal speed, an iterative dynamic linearization approach considering the terminal operation point is devised to formulate the relationship of the train input and output (I/O) into a linear affine form. Then, a terminal iterative learning prediction mechanism is introduced to reconstruct the developed train data model to forecast the future train behaviors through rolling optimization process. As a result, by removing the constraints on the unimportant points, the optimal control input (braking force) can be obtained by minimizing the objective function through terminal I/O data. Further, actuator saturation is considered to address the passenger comfort and the reliable operation of the train. The proposed SATILPC approach is a pure data-driven iterative learning control scheme and no model information is involved in the whole design processes. By utilizing a newly space-based contraction mapping method, the convergence of the proposed approach is strictly proved. Finally, the simulation results further demonstrate the feasibility of the proposed algorithm.

A variable step‐size extrapolated Crank–Nicolson method for option pricing under stochastic volatility model with jump

This paper studies the numerical solution of the stochastic volatility model with jumps under European options. This model can be transformed into a partial integro‐differential equation (PIDE) with spatial mixed derivative terms. Due to the nonsmoothness of the initial function, the variable step‐size extrapolated Crank–Nicolson (CN) method, which explicitly discretizes the jump term and implicitly the rest, is proposed to solve this model. The finite difference method is used to discretize the spatial differential operator, the composite trapezoidal rule to calculate the jump integral, and then a linear system with a nine‐diagonal coefficient matrix is obtained, which is easy to solve. The stability of the variable step‐size extrapolated CN method is then proved. Based on realistic regularity assumptions on the data, the consistency error and global error bounds of the variable step‐size extrapolated CN method are derived in norm. Compared with the variable step‐size IMEX BDF2 method and the variable step‐size IMEX MP method, the numerical results show the efficiency of the proposed variable step‐size extrapolated CN method.

Kinetic-energy- and pressure-equilibrium-preserving schemes for real-gas turbulence in the transcritical regime

Numerical simulations of compressible turbulent flows governed by real-gas equations of state, such as high-pressure transcritical flows, are strongly susceptible to instabilities. In addition to the inherent multi-scale nature of the flow, the presence of a pseudo-interface can generate spurious pressure oscillations when conventional schemes are utilized. This study proposes a general framework to derive and analyze discretization methods that are able to preserve kinetic energy by convection, and simultaneously maintain pressure equilibrium in discontinuity-free compressible real-gas flows. The formal analysis reveals that the discrete pressure-equilibrium condition can be fulfilled at most to second-order accuracy, as it requires the spatial differential operator to satisfy a discrete chain rule when total or internal energy are directly discretized. A novel class of schemes based on the solution of a pressure equation is thus proposed, which preserves mass, momentum, kinetic energy and pressure equilibrium, but not total energy. Extensive numerical tests of increasing complexity confirm the theoretical predictions, and show that the proposed scheme is capable of providing non-dissipative, stable and oscillation-free simulations, unlike existing methods tailored for the transcritical regime.

Integrating by parts at finite density

Both nonzero temperature and chemical potentials break the Lorentz symmetry present in vacuum quantum field theory by singling out the rest frame of the heat bath. This leads to complications in the application of thermal perturbation theory, including the appearance of novel infrared divergences in loop integrals and an apparent absence of four-dimensional integration-by-parts (IBP) identities, vital for high-order computations. Here, we propose a new strategy that enables the use of IBP techniques in the evaluation of Feynman integrals, in particular vacuum or bubble diagrams, in the limit of vanishing temperature T but nonzero chemical potentials μ. The central elements of the new setup include a contour representation for the temporal momentum integral, the use of a small but nonzero T as an IR regulator, and the systematic application of both temporal and spatial differential operators in the generation of linear relations among the loop integrals of interest. The relations we derive contain novel inhomogeneous terms featuring differentiated Fermi-Dirac distribution functions, which severely complicate calculations at nonzero temperature, but are shown to reduce to solvable lower-dimensional objects as T tends to zero. Pedagogical example computations are kept at the one- and two-loop levels, but the application of the new method to higher-order calculations is discussed in some detail.

Comparison principles for the time-fractional diffusion equations with the Robin boundary conditions. Part I: Linear equations

The main objective of this paper is analysis of the initial-boundary value problems for the linear time-fractional diffusion equations with a uniformly elliptic spatial differential operator of the second order and the Caputo type time-fractional derivative acting in the fractional Sobolev spaces. The boundary conditions are formulated in form of the homogeneous Neumann or Robin conditions. First we discuss the uniqueness and existence of solutions to these initial-boundary value problems. Under some suitable conditions on the problem data, we then prove positivity of the solutions. Based on these results, several comparison principles for the solutions to the initial-boundary value problems for the linear time-fractional diffusion equations are derived.

Fractional Schrödinger equation and time dependent potentials

We investigate the solutions for a time-dependent potential by considering two scenarios for the fractional Schrödinger equation. The first scenario analyzes the influence of the time-dependent potential in the absence of the kinetic term. We obtain analytical and numerical solutions for this case by considering the Caputo fractional time derivative, which extends Rabi’s model. In the second scenario, we incorporate the kinetic term in the Schrödinger equation and consider fractional spatial derivatives. For this case, we analyze the spreading of the Gaussian wave package under the action of the time and spatial fractional differential operators.

Spatial adaptive iterative learning control for high-speed train with unknown speed delays

This article focuses on the high-speed train with unknown speed delays; a spatial adaptive iterative learning control (SAILC) algorithm is designed to solve the displacement-speed trajectory tracking problem by using the spatial differential operator to transform the train temporal dynamic model into a spatial model. First, parametric adaptive control is used to reduce the influence of system uncertainties. Second, a Lyapunov-Krasovskii-like spatial composite energy function (SCEF) is established, the stability of the designed controller and the convergence of the tracking error are demonstrated by verifying the differential negative definiteness and boundedness of the function. In addition, the train speed needs to be maintained within a certain range due to the speed loss when it passes through the neutral zone and overspeed protection. Therefore, the state-constrained mechanism is implanted into the train control algorithm to ensure that the train operates within the speed limits. Finally, the proposed SAILC algorithm is compared with proportional-integral-derivative (PID) and iterative learning control (ILC) algorithm; the results of the numerical simulations prove the effectiveness of the proposed method. After 20 iterations, the maximum absolute error of SAILC is 0.05 m/s, which is 4.7% of PID and 10.7% of ILC. It meets the requirements of Automatic Train Operation (ATO) that the tracking error should not be greater than 1% of the train operating speed.

Regularity of weak solutions to a class of fourth order parabolic variational inequality problems arising from swap option pricing

<abstract><p>This article concerns the regularity of weak solutions for a variational inequality problem constructed by a fourth-order parabolic operator which has received much attention recently. We first consider the internal regular estimate of weak solutions using the difference type test function. Then, the near edge regularity and global regularity of weak solutions are analyzed by using the finite cover principle. Since the quadratic gradient of the weak solution does not satisfy the conditions for a test function, we have constructed a test function using a spatial difference operator to complete the proof of regularity. The results show that the weak solution has a second order regularity and an $ {L^\infty }(0, T;{H^2}(\Omega)) $ estimation independent of the lower order norm of the weak one.</p></abstract>

A unified derivation of Voronoi, power, and finite-element Lagrangian computational fluid dynamics

Lagrangian fluid dynamics simulations often start from a definition of the volume of a particle, from which discrete versions of the spatial differential operators are derived. Recently, Gallouët and Mérigot (2018) tackled simultaneously physical dynamics and geometrical optimization, with the result that the pressure field is linked to a geometric feature: the weights of a power diagram.Inspired by this work, simultaneous geometrical and mechanical optimization is here considered, within a framework due to Arroyo and Ortiz (2006). In a systematic way, we first find a connection with the smoothed particle hydrodynamics method. In what we will call the “low-temperature limit”, the requirement of zeroth order consistency leads to the Voronoi diagram, and incompressibility leads to Gallouët and Mérigot’s method.If the requirement of first order consistency is added, the particle finite element method (pFEM) is recovered. However, it features an additional spring-like term that has been missing from previous formulations of the method. Different methods are tested on two standard inviscid single-phase cases: the rotating Gresho vortex and the Taylor–Green vortex sheet, showing the superiority of pFEM, which is slightly increased by the additional force found here.

Generalized SAV-Exponential Integrator Schemes for Allen--Cahn Type Gradient Flows

The energy dissipation law and the maximum bound principle (MBP) are two important physical features of the well-known Allen-Cahn equation. While some commonly-used first-order time stepping schemes have turned out to preserve unconditionally both energy dissipation law and MBP for the equation, restrictions on the time step size are still needed for existing second-order or even higher-order schemes in order to have such simultaneous preservation. In this paper, we develop and analyze novel first- and second-order linear numerical schemes for a class of Allen-Cahn type gradient flows. Our schemes combine the generalized scalar auxiliary variable (SAV) approach and the exponential time integrator with a stabilization term, while the standard central difference stencil is used for discretization of the spatial differential operator. We not only prove their unconditional preservation of the energy dissipation law and the MBP in the discrete setting, but also derive their optimal temporal error estimates under fixed spatial mesh. Numerical experiments are also carried out to demonstrate the properties and performance of the proposed schemes.

Discretization Corrected Particle Strength Exchange for Steady State Linear Elasticity

Discretization corrected particle strength exchange (DC PSE) is a particle based spatial differential operator designed to solve meshless continuum mechanics problems. DC PSE is a spatial gradient operator that can discretize a computational domain with randomly distributed particles, provided that each particle has enough neighboring particles. In contrast, conventional methods such as the standard finite difference method require the computational domain to be discretized into a Cartesian grid. In linear elasticity simulations, especially steady state cases, this domain is mostly discretized using mesh-based methods such as finite element. However, while particle methods such as smoothed particle hydrodynamics (SPH) have been widely applied to solve dynamic elasticity problems, they have rarely been used in steady state simulations. In this study, a DC PSE operator was used to solve steady linear elasticity problems in a two-dimensional domain. The result of the DC PSE numerical simulation was compared to numerical results, empirical formula results, and results from conventional commercial finite element software, respectively.

A stabilizer free spatial weak Galerkin finite element methods for time-dependent convection-diffusion equations

In this paper, we propose a stabilizer free spatial weak Galerkin (SFSWG) finite element method for solving time-dependent convection diffusion equations based on weak form Eq. (4). SFSWG method in spatial direction and Euler difference operator Eq. (37) in temporal direction are used. The main reason for using the SFSWG method is because of its simple formulation that makes this algorithm more efficient and its implementation easier. The optimal rates of convergence of 𝒪⁢(hk) and 𝒪⁢(hk+1) in a discrete H1 and L2 norms, respectively, are obtained under certain conditions if polynomial spaces (Pk⁢(K),Pk⁢(e),[Pj⁢(K)]2) are used in the SFSWG finite element method. Numerical experiments are performed to verify the effectiveness and accuracy of the SFSWG method.

A consistent multiphase flow model with a generalized particle shifting scheme resolved via incompressible SPH

This contribution outlines a multiphase flow solution method developed in conjunction with the incompressible smoothed particle hydrodynamics (SPH) discretization. We present (a) a generalized multiphase particle shifting technique that maintains a regular particle distribution and prevents interface particle mixture; and (b) a color-function-based repulsive force for the pressure gradient term at the correction step of the projection method, which improves the interface smoothness while minimizing the contribution of artificial forces. The solution approach employs consistent discretizations for the gradient and Laplacian operators, which enhances the accuracy of the spatial differential operators. The surface tension model embraced relies on the continuum surface stress (CSS) formulation. The solution methodology proposed is broadly applicable since i) the generalized particle shifting scheme applies to both 2D and 3D simulations, and ii) the interface smoothness and the solution robustness can be preserved with density ratios up to 1:1000, and viscosity ratios up to 1:100. The solution method has been used in conjunction with a set of benchmark problems to demonstrate its accuracy by comparing simulation results against analytical solutions, results from other numerical methods, and experimental data. The method has been implemented to leverage GPU-enabled parallelism and used herein to solve complex physics phenomena encountered in 3D bubble rising and coalescence problems via simulations that include over one million SPH particles.

Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising

The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal. However, since SSTV computes local spatial differences uniformly, it is difficult to remove noise while preserving complex spatial structures with fine edges and textures, especially in situations of high noise intensity. To solve this problem, we propose a new TV-type regularization called Graph-SSTV (GSSTV), which generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs and incorporates a weighted spatial difference operator designed based on this graph. Furthermore, we formulate the mixed noise removal problem as a convex optimization problem involving GSSTV and develop an efficient algorithm based on the primal-dual splitting method to solve this problem. Finally, we demonstrate the effectiveness of GSSTV compared with existing HSI regularization models through experiments on mixed noise removal. The source code will be available at https://www.mdi.c.titech.ac.jp/publications/gsstv.

Minimal residual space-time discretizations of parabolic equations: Asymmetric spatial operators

We consider a minimal residual discretization of a simultaneous space-time variational formulation of parabolic evolution equations. Under the usual ‘LBB’ stability condition on pairs of trial- and test spaces we show quasi-optimality of the numerical approximations without assuming symmetry of the spatial part of the differential operator. Under a stronger LBB condition we show error estimates in an energy-norm that are independent of this spatial differential operator.

An efficient variable step-size method for options pricing under jump-diffusion models with nonsmooth payoff function

We develop an implicit–explicit midpoint formula with variable spatial step-sizes and variable time step to solve parabolic partial integro-differential equations with nonsmooth payoff function, which describe the jump-diffusion option pricing model in finance. With spatial differential operators being treated by using finite difference methods and the jump integral being computed by using the composite trapezoidal rule on a non-uniform space grid, the proposed method leads to linear systems with tridiagonal coefficient matrices, which can be solved efficiently. Under realistic regularity assumptions on the data, the consistency error and the global error bounds for the proposed method are obtained. The stability of this numerical method is also proved by using the Von Neumann analysis. Numerical results illustrate the effectiveness of the proposed method for European options under jump-diffusion models.

A space-time certified reduced basis method for quasilinear parabolic partial differential equations

In this paper, we propose a certified reduced basis (RB) method for quasilinear parabolic problems with strongly monotone spatial differential operator. We provide a residual-based a posteriori error estimate for a space-time formulation and the corresponding efficiently computable bound for the certification of the method. We introduce a Petrov-Galerkin finite element discretization of the continuous space-time problem and use it as our reference in a posteriori error control. The Petrov-Galerkin discretization is further approximated by the Crank-Nicolson time-marching problem. It allows to use a POD-Greedy approach to construct the reduced-basis spaces of small dimensions and to apply the Empirical Interpolation Method (EIM) to guarantee the efficient offline-online computational procedure. In our approach, we compute the reduced basis solution in a time-marching framework while the RB approximation error in a space-time norm is controlled by our computable bound. Therefore, we combine a POD-Greedy approximation with a space-time Galerkin method.

Sinc Numerical Methods for Time Nonlocal Parabolic Equation

In recent years, more and more researchers have paid attention to the study of non-local problems. The numerical method for initial-boundary value problems of time nonlocal parabolic equations is established in this paper. The time nonlocal operator is discretized by finite difference method, and spatial differential operators is discretized by Sinc-Galerkin method. Then fully discrete scheme (D-SD scheme) for solving one-dimensional time nonlocal parabolic equation is obtained. Numerical example shows the effectiveness and superiority of the scheme for solving non-local problems.

An implicit difference scheme for time-fractional diffusion equations with a time-invariant type variable order

In this paper, we study a time-fractional diffusion equation with a time-invariant type variable fractional order. We propose an implicit finite difference scheme to approximate the variable-order Caputo fractional derivative, while the central difference method is employed to discretize the spatial differential operator. A novel decomposition of the temporal discretization coefficients is adopted to overcome their loss of monotonicity due to the impact of the variable order and thus to support the proof of the convergence and unconditionally stability of the numerical scheme. Numerical examples are presented to verify the effectiveness of the proposed method.