Κατεύθυνση / ειδίκευση Επεξεργασία-Μάθηση Σήματος και Πληροφορίας (ΕΜΠ)
Πληροφορική

2024-09-16

2024

Papadopoulos Aristeidis

Δημήτριος Συβρίδης, Καθηγητής, Τμήμα Πληροφορικής και Επικοινωνιών, ΕΚΠΑ
Ηλίας Γλύτσης, Καθηγητής, Σχολή ΗΜΜΥ, ΕΜΠ

A Sparse Polynomial Chaos Algorithm with a Variance-Adaptive Design Domain for the Uncertainty Quantification and Optimization of Grating Structures

English

A Sparse Polynomial Chaos Algorithm with a Variance-Adaptive Design Domain for the Uncertainty Quantification and Optimization of Grating Structures

In this work, we introduce an algorithm based on Polynomial-Chaos Expansions (PCE) to
tackle uncertainty quantification problems related to grating filters. Our approach adaptively
constructs anisotropic PC models for the quantities of interest, accommodating varying
polynomial orders. It exploits the sparsity of the PCE coefficients, which are computed
using the Least Angles Regression (LARS) sparse solver, leading to a highly efficient process.
Additionally, we design optimal experiments that leverage the local variance of the
samples, further improving the reliability of the computations. We apply this method to
the uncertainty quantification of a typical resonant grating filter, demonstrating its superior
efficiency compared to standard techniques. In addition, the constructed PCE model can
generate samples of the grating filter’s quantities of interest, which can be used alongside
a stochastic optimizer to optimize the grating filter’s performance concerning its design
variables. Furthermore improved optimization results are observed when the presented
PCE algorithm is combined with Kriging interpolation.

Technology - Computer science

Adaptive algorithm, grating filters, sparse polynomial chaos, uncertainty quantification, least angle regression, variance-adaptive design, Kriging interpolation, particle swarm optimization.

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File access is restricted until 2025-09-16.

https://pergamos.lib.uoa.gr/uoa/dl/object/3415371