Robust Model Reduction for High-contrast Problems

Prelegent(ci)

Marcus Sarkis

Afiliacja

WPI, USA

Termin

26 września 2019 10:30

Pokój

p. 5840

Seminarium

Seminarium Zakładu Analizy Numerycznej

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Major progress has been made recently to make preconditioners robust with respect to variation of coefficients. A reason for this success is the adaptive selection of primal constraints based on localized generalized eigenvalue problems. In this talk we discuss how to transfer this technique to the field of discretizations. Given a target accuracy, we design a robust model reduction by delocalizing multiscale basis functions and establish a priori energy error estimates with such target accuracy with hidden constants independently of the coefficients. This is a joint work with Alexandre Madureira from LNCC, Brazil.

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