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Technical University of Munich
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Discovering stability and receptivity in complex systems no longer requires known equations, thanks to a new neural operator framework that learns directly from data.
Achieving a staggering 84.2% reduction in prediction error with just 450 samples could revolutionize aerodynamic design processes.
Sampling high-resolution fluid flow distributions just got 7x faster, thanks to a hierarchical coarse-to-fine generative model that concentrates computation where uncertainty is highest.
