Robust algorithms for solving stochastic partial differential equations

A robust semi-implicit central partial difference algorithm for the numerical solution of coupled stochastic parabolic partial differential equations (PDEs) is described. This can be used for calculating correlation functions of systems of interacting stochastic fields. Such field equations can arise in the description of Hamiltonian and open systems in the physics of nonlinear processes, and may include multiplicative noise sources. The algorithm can be used for studying the properties of nonlinear quantum or classical field theories. The general approach is outlined and applied to a specific example, namely the quantum statistical fluctuations of ultra-short optical pulses in chi((2)) parametric waveguides. This example uses a non-diagonal coherent state representation, and correctly predicts the sub-shot noise level spectral fluctuations observed in homodyne detection measurements. It is expected that the methods used wilt be applicable for higher-order correlation functions and other physical problems as well. A stochastic differencing technique for reducing sampling errors is also introduced. This involves solving nonlinear stochastic parabolic PDEs in combination with a reference process, which uses the Wigner representation in the example presented here. A computer implementation on MIMD parallel architectures is discussed. (C) 1997 Academic Press.

Differential disease reactions on lucerne genotypes inoculated with Phytophthora medicaginis isolates from lucerne and chickpea

Stem inoculation of clonally propagated lucerne genotypes was used to assess levels of host species and genotype specialisation in Phytophthora medicaginis. A quantitative assessment of pathogenic aggressiveness of 29 P. medicaginis isolates (from lucerne and chickpea) on 9 different clonally propagated lucerne genotypes revealed no significant difference in aggressiveness between isolates from lucerne and those from chickpea on all of the lucerne genotypes. This supported previous studies which showed that P. medicaginis isolates from lucerne and chickpea were indistinguishable using random amplified polymorphic DNA (RAPD) analysis. Analysis of pathogenic aggressiveness towards individual lucerne genotypes revealed, for the first time, specificity of individual P. medicaginis isolates. This has implications for breeding for resistance to P. medicaginis in lucerne, where screening should be done using the widest range of pathogen specificity obtainable.

Differential expression of mitochondrial NADH dehydrogenase in ethanol-treated rat brain: Revealed by differential display

The technique of polymerase chain reaction (PCR) differential display was used to detect alterations in gene expression after chronic alcohol administration. Male Wistar rats were treated with ethanol vapor for 14 days. The cDNA generated from mRNA isolated from the hippocampi of ethanol-treated and control animals was compared by PCR differential display. A differentially expressed cDNA fragment was used to screen mRNA samples by Northern analysis. The level of a mRNA was significantly elevated (x 2.5) in the hippocampus, but not the cortex of alcohol-treated rats up to 48 hr after withdrawal. Sequence analysis of the cDNA fragment revealed an almost perfect homology to rat mitochondrial NADH dehydrogenase subunit 4 mRNA. The selective induction of this mRNA in alcohol-treated rat brain areas suggests altered metabolic processes and possible dysfunction of the mitochondria. The technique of PCR differential display may prove useful in further analysis of gene expression during alcohol dependence and withdrawal.

Differential expression of Egr-1-like DNA-binding activities in the naive rat brain and after excitatory stimulation

Egr-1 and related proteins are inducible transcription factors within the brain recognizing the same consensus DNA sequence. Three Egr DNA-binding activities were observed in regions of the naive rat brain. Egr-1 was present in all brain regions examined. Bands composed, at least in part, of Egr-2 and Egr-3 were present in different relative amounts in the cerebral cortex, striatum, hippocampus, thalamus, and midbrain. All had similar affinity and specificity for the Egr consensus DNA recognition sequence. Administration of the convulsants NMDA, kainate, and pentylenetetrazole differentially induced Egr-1 and Egr-2/3 DNA-binding activities in the cerebral cortex, hippocampus, and cerebellum. All convulsants induced Egr-1 and Egr-2 immunoreactivity in the cerebral cortex and hippocampus. These data indicate that the members of the Egr family are regulated at different levels and may interact at promoters containing the Egr consensus sequence to fine tune a program of gene expression resulting from excitatory stimuli.

The composite Euler method for stiff stochastic differential equations

In this paper we present the composite Euler method for the strong solution of stochastic differential equations driven by d-dimensional Wiener processes. This method is a combination of the semi-implicit Euler method and the implicit Euler method. At each step either the semi-implicit Euler method or the implicit Euler method is used in order to obtain better stability properties. We give criteria for selecting the semi-implicit Euler method or the implicit Euler method. For the linear test equation, the convergence properties of the composite Euler method depend on the criteria for selecting the methods. Numerical results suggest that the convergence properties of the composite Euler method applied to nonlinear SDEs is the same as those applied to linear equations. The stability properties of the composite Euler method are shown to be far superior to those of the Euler methods, and numerical results show that the composite Euler method is a very promising method. (C) 2001 Elsevier Science B.V. All rights reserved.

Implicit Taylor methods for stiff stochastic differential equations

In this paper we discuss implicit Taylor methods for stiff Ito stochastic differential equations. Based on the relationship between Ito stochastic integrals and backward stochastic integrals, we introduce three implicit Taylor methods: the implicit Euler-Taylor method with strong order 0.5, the implicit Milstein-Taylor method with strong order 1.0 and the implicit Taylor method with strong order 1.5. The mean-square stability properties of the implicit Euler-Taylor and Milstein-Taylor methods are much better than those of the corresponding semi-implicit Euler and Milstein methods and these two implicit methods can be used to solve stochastic differential equations which are stiff in both the deterministic and the stochastic components. Numerical results are reported to show the convergence properties and the stability properties of these three implicit Taylor methods. The stability analysis and numerical results show that the implicit Euler-Taylor and Milstein-Taylor methods are very promising methods for stiff stochastic differential equations.

Three-point boundary value problems for second-order, ordinary, differential equations

We establish existence results for solutions to three-point boundary value problems for nonlinear, second-order, ordinary differential equations with nonlinear boundary conditions. (C) 2001 Elsevier Science Ltd. All rights reserved.

Differential inclusions and robust control theory

Uncontrolled systems (x) over dot is an element of Ax, where A is a non-empty compact set of matrices, and controlled systems (x) over dot is an element of Ax + Bu are considered. Higher-order systems 0 is an element of Px - Du, where and are sets of differential polynomials, are also studied. It is shown that, under natural conditions commonly occurring in robust control theory, with some mild additional restrictions, asymptotic stability of differential inclusions is guaranteed. The main results are variants of small-gain theorems and the principal technique used is the Krasnosel'skii-Pokrovskii principle of absence of bounded solutions.

Stability of linear difference and differential inclusions

Any given n X n matrix A is shown to be a restriction, to the A-invariant subspace, of a nonnegative N x N matrix B of spectral radius p(B) arbitrarily close to p(A). A difference inclusion x(k+1) is an element of Ax(k), where A is a compact set of matrices, is asymptotically stable if and only if A can be extended to a set B of nonnegative matrices B with \ \B \ \ (1) < 1 or \ \B \ \ (infinity) < 1. Similar results are derived for differential inclusions.

Stiffly accurate Runge-Kutta methods for stiff stochastic differential equations

In this paper we discuss implicit methods based on stiffly accurate Runge-Kutta methods and splitting techniques for solving Stratonovich stochastic differential equations (SDEs). Two splitting techniques: the balanced splitting technique and the deterministic splitting technique, are used in this paper. We construct a two-stage implicit Runge-Kutta method with strong order 1.0 which is corrected twice and no update is needed. The stability properties and numerical results show that this approach is suitable for solving stiff SDEs. (C) 2001 Elsevier Science B.V. All rights reserved.