An Artificial Intelligence–Assisted Design Method for Topology Optimization without Pre-Optimized Training Data

Engineers widely use topology optimization during the initial process of product development to obtain a first possible geometry design. The state-of-the-art method is iterative calculation, which requires both time and computational power. This paper proposes an AI-assisted design method for topology optimization, which does not require any optimized data. An artificial neural network—the predictor—provides the designs on the basis of boundary conditions and degree of filling as input data. In the training phase, the so-called evaluators evaluate the generated geometries on the basis of random input data with respect to given criteria. The results of those evaluations flow into an objective function, which is minimized by adapting the predictor’s parameters. After training, the presented AI-assisted design procedure generates geometries that are similar to those of conventional topology optimizers, but require only a fraction of the computational effort. We believe that our work could be a clue for AI-based methods that require data that are difficult to compute or unavailable.

Spectral Stochastic Infinite Element Method in Vibroacoustics

A novel method to solve exterior Helmholtz problems in the case of multipole excitation and random input data is developed. The infinite element method is applied to compute the sound pressure field in the exterior fluid domain. The consideration of random input data leads to a stochastic infinite element formulation. The generalized polynomial chaos expansion of the random data results in the spectral stochastic infinite element method. As a solution technique, the non-intrusive collocation method is chosen. The performance of the spectral stochastic infinite element method is demonstrated for a time-harmonic problem and an eigenfrequency study.

Computational Probabilistic Analysis and Transformation Method in the Earth Remote Research

The article deals with the issues of numerical modeling of problems with random input data. Finding the joint probability density function of the vector of output parameters is considered. It is proposed to use computational probabilistic analysis and the transformation method. A numerical example of the joint probability density function of the vector of a solution of a system of nonlinear equations with random input data is given.

Numerical Analysis for Problems of Remote Sensing with Random Input Data

The study is devoted to the remote sensing data processing using the models with random input data. In this article we propose a new approach to calculation of functions with random arguments, which is a technique of fast computations, based on the idea of parallel computations and the application of numerical probability analysis. To calculate a function with random arguments we apply one of the basic concepts of numerical probabilistic analysis as the probabilistic extension. To implement the technique of fast computations, a new method based on parallel recursive calculations is proposed.