Reduced-order modeling: new approaches for computational physics

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Evaluation of aerodynamic loads via reduced order methodology (2015, methodology): “of approximation is found in the process of projecting the solution of theNavier–Stokes equations onto the space of the coherent structures [2,5] and in the method used to compute the modal amplitudes for each mode [7,12,16]) are well-documented approaches with the advantages and disadvantages, in t”
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets (2021, positive): “This challenge has motivated a growing literature on reduced-order methods [3, 4, 5, 6, 7, 8, 9] that leverage existing data-sets to build fast emulators, often at the price of reduced accuracy, stability, and generalization performance [10, 11].”
Model Order Reduction in Fluid Dynamics: Challenges and Perspectives (2014, background): “For a comparison between POD and HB we refer to [82].”