arXiv:2606.28871v1 Announce Type: cross Abstract: Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and direct simulations are prohibitive. A pragmatic way to overcome this limitation is by calibrating low-fidelity computational predictions with experimental measurements. This, however, requires calibrating against \emph{sparse} measurements contaminated with \emph{uncertainty} in both the control inputs and the measured aerodynamic response. We develop a methodology to address this problem based o
The increasing complexity of aerodynamic simulations and the growing need for efficient and accurate uncertainty quantification in design processes drive the development of advanced AI/ML methods.
This development can significantly improve the design and performance prediction of aircraft and other complex systems, reducing development cycles and costs while enhancing safety and efficiency.
The ability to more accurately calibrate low-fidelity computational models with sparse, uncertain experimental data changes how engineering design and validation are approached, relying less on expensive direct simulations.
- · Aerospace & Defence Industry
- · Physics-informed AI/ML researchers
- · Computational Fluid Dynamics (CFD) engineers
- · Aircraft manufacturers
- · Traditional high-fidelity simulation software without AI integration
- · Organizations relying solely on extensive physical prototyping
Improved accuracy and efficiency in aerodynamic design and optimization processes using Bayesian methods.
Faster innovation cycles in aerospace and related fields due to more reliable computational modeling and reduced experimental needs.
Potential for AI-driven autonomous design systems that can rapidly iterate and optimize complex physical systems with built-in uncertainty quantification.
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Read at arXiv cs.LG