Design Optimization Using Artificial Intelligence
Overview
Traditional design optimization faces critical limitations: exhaustive exploration of vast design spaces is impossible, simultaneous maximization of conflicting performance metrics is elusive, and high-fidelity simulations (FEA/CAE/CFD/Circuit) are computationally prohibitive. This leads to sub-optimal designs and prolonged development cycles.
To address these challenges, we will demonstrate how AI-powered surrogate modeling addresses these challenges, enabling rapid design optimization. Using a vehicle suspension system as a practical case study, participants will learn to:
- Implement Design of Experiments (DoE) strategies to create robust training datasets that accurately represent the multi-dimensional design space, ensuring comprehensive model coverage.
- Build AI-based surrogate models that accurately emulate complex, nonlinear relationships between design parameters and performance metrics, replacing computationally expensive simulations with near real-time predictions.
- Utilize surrogate models to conduct rapid 'what-if' analyses, visualizing and quantifying the impact of design parameter variations on critical performance objectives, facilitating informed decision-making
- Demonstrate the ability of AI models to quickly adapt to changing design requirements and constraints, enabling rapid re-optimization and minimizing design iterations.
Who Should Attend
This presentation is ideal for engineers, designers, project managers, and anyone interested in leveraging AI for design optimization. Whether you're dealing with mechanical, electrical, or systems design, this session will provide valuable strategies to enhance your projects.
This event is part of a series of related topics. View the full list of events in this series.
