## Overview

Via this page you obtain a slow manifold of any supplied
stochastic differential equation (SDE), or
deterministic, autonomous or non-autonomous, ODE, when the
SDE has fast and slow modes. The slow
manifold supplies you with a faithful large time model of
the stochastic dynamics. Being justified by a normal form
coordinate transform you are assured that the dynamics are
attractive over some finite domain and apply for all time.

For example, this web page could help you analyse the stochastic bifurcation in the Stratonovich stochastic system

- dx/dt=epsilon*x-x*y ,
- dy/dt=-y+x^2-2y^2+w(t) ,

## Submit your SDE for analysis

Fill in the fields below for your SDE system:

- your m slow variables must be denoted x(1),...,x(m);
- your ny fast stable variables must be denoted y(1),...,y(ny);
- your nz fast unstable variables must be denoted z(1),...,z(nz);
- the fast variables must be linearly decoupled, that is, the linear dynamics have been diagonalised; each of the linear decay/growth rates of the fast variables must be a positive number;
- any number of Stratonovich white noises (derivatives of
Wiener processes) or non-autonomous time dependent factors
must be denoted
`w(any)`where `any' denotes almost any label of your choice---numeric labels are usual; - the noises
`w()`must occur linearly in the RHSs of the SDEs, but may be multiplied by fast or slow variables; - simply omit all
`w()`s to analyse an autonomous system of ODEs; - for the moment, the SDEs must be either multinomial in form or a rational multinomial---if the latter, then multiply through by a common denominator transfer to the RHS the nonlinear terms involving time derivatives, to end up with a multinomial RHS that includes nonlinear terms with a time derivative factor;
- Use the syntax of Reduce for the algebraic expressions

## Wait a minute or two

The analysis may take minutes after submitting. Be patient.
Read the following. Please inform me of any problems.

### In the results

- Each
`xx(i)`denotes the true slow variable X(i) where the original x(i)=X(i)+(nonlinear modifications). `ou(w,tt,r)`denotes convolution over the Stratonovich process`w`(not Ito):`ou(w,tt,r)=exp(rt)*w`where the asterisk`*`denotes convolution which is done over the past history of the autonomous forcing/Stratonovich process`w`as`r<0`.- For explanations and relevant theory, see my articles Normal form transforms separate slow and fast modes in stochastic dynamical systems [doi:10.1016/j.physa.2007.08.023] and Computer algebra derives normal forms of stochastic differential equations.

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