****** Registration would be recommended ******
Quantum Machine Learning in Aerospace
Quantum Computing is a novel computation paradigm with several proven computational speed ups and promising novel applications.
The DLR institute for AI Safety and Security is investigating the potential of machine learning methods on quantum computers and brings them closer to real world use cases -- together with their partners from industry. Quantum Machine Learning can leverage quantum superposition and entanglement to explore vast high‑dimensional data spaces far more efficiently than classical algorithms, it promises novel insights that make it worth investigating for a wide range of practical applications.
Pedro Alves presents two distinct quantum machine learning architectures, designed for aeroelastic use cases: a hybrid quantum tensor network based algorithm for estimating flutter stability and a quantum surrogate physical model to predict aerodynamical variables for aircraft design.
This event will be streamed online. The link will be uploaded shortly before the presentation starts.
THE aerospace TALKS is a lecture series organized by the Department of Aerospace Engineering and Geodesy. Internal and external guests from academia, industry, and politics provide insights into current topics and developments in aerospace—for both experts and the general public.
