Speaker
Description
Numerical investigations of real engineering problems have become indispensable due to the continuously increasing complexity of the issues at hand. Especially when analytical solution methods reach their limits and experimental investigations are associated with high effort and costs, simulation offers a pragmatic and promising approach. The development of the finite element method, which was only made possible in the last century due to the available computing power, is now a standard tool in engineering practice.
Engineering applications are diverse, and simulations can become very complex. Considering geometric and physical nonlinearities, time-varying simulations, multiscale modeling, multicriteria optimization, or analyses with uncertain variables require adequate computer-based models. It is often practical and sometimes necessary to replace the actual model with more efficient ones, ensuring the loss of accuracy is minimized. One approach that will continue to gain importance in the future is AI-based surrogate modeling, utilizing and combining different neural networks. Even in choosing the appropriate networks, such as FFNN, RNN, CNN, and their corresponding architectures, artificial intelligence can assist, taking into account available data for a specific problem.
Beyond the solution phase, where defined output parameters are determined based on available input parameters using an AI-based model, the use of artificial intelligence in the preprocessing and postprocessing steps leads to a holistic approach. At the conference, we will present and discuss how model creation in the preprocessing and model evaluation in the postprocessing can be supported by AI. An automated process will be presented using academic examples with various engineering-related questions.
