Adaptive Multi-Fidelity Design Optimization

A case study using Optimus, an automation and optimization tool by Noesis Solutions

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Significantly reduce computational cost while increasing accuracy with Optimus

Challenges of modern engineering

Modern engineering design processes face the challenge of integrating data from diverse sources into consistent predictive models, known as multi-fidelity models. This integration offers advantages like reduced costs and improved design quality. However, a key challenge is developing algorithms that automatically identify the best distribution of design investigations, considering the compromise between sampling cost and desired accuracy.

Optimus integration

This case study presents a machine-learning algorithm that addresses the challenge by guiding numerical experiments at different fidelity levels, adaptively selecting sampling locations, and optimizing the design using Optimus.

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