Master Thesis Project: Evaluation of AI-based Surrogate Models for Structural CAE Analysis

Background
Artificial Intelligence is rapidly being introduced into engineering simulation workflows. One promising application is AI-based surrogate models that can predict simulation results significantly faster than traditional finite element analyses. This thesis aims to evaluate the applicability and potential efficiency gains of surrogate models in structural CAE analysis.
Objective
The study should investigate:
Prediction accuracy compared to traditional FE simulations
Required amount of training data
Potential reduction in simulation turnaround time
Applicability for implicit and explicit analyses
The final objective is to establish guidelines for when AI-based surrogate models should be used and when traditional FE simulations remain the preferred approach.
Methods
The study will use a finite element structure model as a case study. Surrogate models will be developed using data from both implicit and explicit analyses. The predictive accuracy, computational efficiency and required engineering effort will be compared with conventional FE simulations.
Deliverables
A report containing:
 Evaluation of AI-based surrogate models for structural CAE
 Assessment of accuracy and potential time savings
 Evaluation of applicability for implicit and explicit analyses
 Guidelines and recommendations for industrial use
Master Student at Applied Mechanics or equivalent program. Thesis project will be performed in Trollhättan.

Employment type
Intern

Work arrangement
No