We proposed a new iterative power and amplitude correction (IPAC) algorithm to simulate nonstationary and non-Gaussian processes. The proposed algorithm is rooted in the concept of defining the stochastic processes in the transform domain, which is elaborated and extend. The algorithm extends the iterative amplitude adjusted Fourier transform algorithm for generating surrogate and the spectral correction algorithm for simulating stationary non-Gaussian process. The IPAC algorithm can be used with different popular transforms, such as the Fourier transform, S-transform, and continuous wavelet transforms. The targets for the simulation are the marginal probability distribution function of the process and the power spectral density function of the process that is defined based on the variables in the transform domain for the adopted transform. The algorithm is versatile and efficient. Its application is illustrated using several numerical examples.

中文翻译：

---

一种模拟非平稳和非高斯随机过程的算法

我们提出了一种新的迭代功率和幅度校正 (IPAC) 算法来模拟非平稳和非高斯过程。所提出的算法植根于在变换域中定义随机过程的概念，对其进行了阐述和扩展。该算法扩展了用于生成代理的迭代幅度调整傅立叶变换算法和用于模拟平稳非高斯过程的频谱校正算法。IPAC 算法可用于不同的流行变换，例如傅立叶变换、S 变换和连续小波变换。模拟的目标是过程的边际概率分布函数和过程的功率谱密度函数，该函数是基于所采用变换的变换域中的变量定义的。该算法通用且高效。它的应用通过几个数值例子来说明。