Workshop Description

Multifidelity modeling encompasses a broad range of methods that use approximate models together with high-fidelity models to accelerate a computational task that requires repeated model evaluations. This workshop will highlight the tremendous recent progress of multifidelity methods for design optimization and uncertainty quantification, including (but not limited to) methods based on adaptive sampling, control variate formulations, importance sampling, trust region model management, model fusion, and Bayesian optimization. The focus is on a tutorial-style series of lectures aimed at the practitioner, together with forward-looking discussions of challenges and opportunities. The workshop will include the following key discussion topics:

1) multifidelity formulations that combine computational models with other sources of information, such as experimental data and expert opinion;

2) exploiting the connections between multifidelity modeling and machine learning methods;

3) past successes of applying multifidelity modeling in aircraft design, structural modeling, and other fields;

4) future opportunities in areas such as material design and autonomous systems.

Workshop Objectives

• Dissemination of recent methods developments to the MDO practitioner community.
• Discussion of challenges and opportunities to identify new collaborations and new research directions.

Sponsored by the AIAA Multidiscipliary Design Optimization Technical Committee (MDO TC)

Workshop Venue: