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Publisher Correction: Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design.

Scientific reports·2024

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Updated: Jul 8, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design.

Francesco Di Fiore¹, Laura Mainini^2,3

¹Departement of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129, Turin, Italy.

Scientific Reports

|December 18, 2023

Summary

This summary is machine-generated.

This study introduces Non-Myopic Multipoint Multifidelity Bayesian Optimization (NM3-BO) to accelerate multidisciplinary design optimization (MDO). NM3-BO enables parallel computations and considers long-term gains, outperforming existing methods.

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Area of Science:

Engineering
Computational Science
Optimization

Background:

High-fidelity models in multidisciplinary design optimization (MDO) enhance design identification but demand significant computational resources.
Multifidelity Bayesian Optimization (MFBO) accelerates MDO by integrating models at various fidelity levels.
Current MFBO methods face limitations in parallel high-fidelity computations and myopic (short-sighted) search strategies.

Purpose of the Study:

To develop a novel algorithm, Non-Myopic Multipoint Multifidelity Bayesian Optimization (NM3-BO), to accelerate MDO.
To overcome the limitations of sequential sampling and immediate-iteration utility assessment in existing MFBO techniques.
To enhance the efficiency of identifying superior design configurations in complex engineering problems.

Main Methods:

Proposed the NM3-BO algorithm, which selects batches of promising designs for parallel evaluation.
Developed a learning scheme with an acquisition function incorporating a two-step lookahead policy.
Integrated a local penalization strategy to assess the simultaneous evaluation utility of multiple designs.

Main Results:

The NM3-BO algorithm significantly accelerates the MDO process.
The proposed framework demonstrates superior performance compared to popular existing algorithms.
Demonstrated effective acceleration in the MDO of a space vehicle design.

Conclusions:

NM3-BO enhances MDO efficiency by enabling parallel computations and considering long-term optimization benefits.
The novel acquisition function effectively quantifies the future utility of multipoint evaluations.
The algorithm offers a promising approach for accelerating complex design optimization tasks.