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Surrogate modeling of resonant behavior in scattering problems through adaptive rational approximation and sketching
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This paper describes novel algorithms for the identification of (almost-)resonant behavior in scattering problems. Our methods, relying on rational approximation, aim at building surrogate models of what we call "field amplification", defined as the norm of the solution operator of the scattering problem, which we express through boundary-integral equations. To provide our techniques with theoretical foundations, we first derive results linking the field amplification to the spectral properties of the operator that defines the scattering problem. Such results are then used to justify the use of rational approximation in the surrogate-modeling task. Some of our proposed methods apply rational approximation in a "standard" way, building a rational approximant for either the solution operator directly or, in the interest of computational efficiency, for a randomly "sketched" version of it. Our other "hybrid" approaches are more innovative, combining rational-approximation-assisted root-finding with approximation using radial basis functions. Three key features of our methods are that (i) they are agnostic of the strategy used to discretize the scattering problem, (ii) they do not require any computations involving non-real wavenumbers, and (iii) they can adjust to different settings through the use of adaptive sampling strategies. We carry out some numerical experiments involving 2D scatterers to compare our approaches. In our tests, two of our approaches (one standard, one hybrid) emerge as the best performers, with one or the other being preferable, depending on whether emphasis is placed on accuracy or efficiency.
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Efficient time-domain scattering synthesis via frequency-domain singularity subtraction
A frequency-domain singularity subtraction technique, with real-frequency-only resonance identification via rational approximation, enables efficient long-time time-domain scattering simulations for trapping obstacles.
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