Simulation of stationary random waves

By the WAT toolbox one can simulate a large number of stationary random waves and other stochastic processes. Thus one can simulate

Gaussian waves with specified power spectral density. The standard Pierson-Moskowitz and the JONSWAP spectrum are built in functions but the user can define own spectra. It is also possible to define the process via its covariance or correlation function and transform to spectral definition.

Transformed Gaussian waves with horizontal asymmetry.

Duffing oscillators and other second order non-linear oscillators with Gaussian or stable innovations.

Wave process with given irregularity factor and specified crossing spectrum. The irregularity factor is defined as the ratio between the number of mean level up-crossings and the number of local maxima.

Markov chains with arbitrary transition matrix and Markov sequence of local maxima and minima (turning points).

WAT examples

	Gaussian waves with specified power spectral density. The standard Pierson-Moskowitz and the JONSWAP spectrum are built in functions but the user can define own spectra. It is also possible to define the process via its covariance or correlation function and transform to spectral definition.
	Transformed Gaussian waves with horizontal asymmetry.
	Duffing oscillators and other second order non-linear oscillators with Gaussian or stable innovations.
	Wave process with given irregularity factor and specified crossing spectrum. The irregularity factor is defined as the ratio between the number of mean level up-crossings and the number of local maxima.
	Markov chains with arbitrary transition matrix and Markov sequence of local maxima and minima (turning points).