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Progressive multi-fidelity learning for physical system predictions
Highly accurate datasets from numerical or physical experiments are often expensive and time-consuming to acquire, posing a significant …
Paolo Conti, Mengwu Guo, Attilio Frangi, Andrea Manzoni
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Progressive multi-fidelity learning for physical system predictions
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification
The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial …
Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz
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VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification