Text

A Multifidelity Function-on-Function Model Applied to an Abdominal Aortic Aneurysm

In this work, we predict the outcomes of high fidelity multivariate computer simulations from low fidelity counterparts using function-to-function regression. The high fidelity simulation takes place on a high definition mesh, while its low fidelity counterpart takes place on a coarsened and truncated mesh. We showcase our approach by applying it to a complex finite element simulation of an abdominal aortic aneurysm which provides the displacement field of a blood vessel under pressure. In order to link the two multidimensional outcomes we compress them and then fit a function-to-function regression model. The data are high dimensional but of low sample size, meaning that only a few simulations are available, while the output of both low and high fidelity simulations is in the order of several thousands. To match this specific condition our compression method assumes a Gaussian Markov random field that takes the finite element geometry into account and only needs little data. In order to solve the function-to-function regression model we construct an appropriate prior with a shrinkage parameter which follows naturally from a Bayesian view of the Karhunen–Loève decomposition. Our model enables real multivariate predictions on the complete grid instead of resorting to the outcome of specific points.

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.64 No.3 August 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 279-290

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FUNCTIONAL DATA ANALYSIS
COMPUTER EXPERIMENTS
SPATIAL STATISTICS
MULTIVARIATE DATA
GAUSSIAN MARKOV RANDOM FIELDS
GAUSSIAN MARKOV RANDOM
MULTIFIDELITY SIMULATIONS

Info Detail Spesifik

https://doi.org/10.1080/00401706.2021.2024453

Pernyataan Tanggungjawab

Christoph Striegel, Jonas Biehler, Wolfgang A. Wall, Goran Kauermann

Versi lain/terkait

Lampiran Berkas

Komentar