SwePub
Sök i LIBRIS databas

  Utökad sökning

onr:"swepub:oai:DiVA.org:uu-472109"
 

Sökning: onr:"swepub:oai:DiVA.org:uu-472109" > Tailoring Gaussian ...

Tailoring Gaussian processes and large-scale optimisation

Jidling, Carl, 1992- (författare)
Uppsala universitet,Avdelningen för systemteknik,Reglerteknik
Schön, Thomas B., Professor, 1977- (preses)
Uppsala universitet,Avdelningen för systemteknik,Reglerteknik,Artificiell intelligens
Wahlström, Niklas, 1984- (preses)
Uppsala universitet,Reglerteknik,Avdelningen för systemteknik
visa fler...
Wills, Adrian (preses)
University of Newcastle, School of Engineering, Callaghan NSW, Australia
Kai Hansen, Lars, Professor (opponent)
Technical University of Denmark, Department of Applied Mathematics and Computer Science, Kgs Lyngby, Denmark
visa färre...
 (creator_code:org_t)
ISBN 9789151314914
Uppsala : Acta Universitatis Upsaliensis, 2022
Engelska 90 s.
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)
Abstract Ämnesord
Stäng  
  • This thesis is centred around Gaussian processes and large-scale optimisation, where the main contributions are presented in the included papers.Provided access to linear constraints (e.g. equilibrium conditions), we propose a constructive procedure to design the covariance function in a Gaussian process. The constraints are thereby explicitly incorporated with guaranteed fulfilment. One such construction is successfully applied to strain field reconstruction, where the goal is to describe the interior of a deformed object. Furthermore, we analyse the Gaussian process as a tool for X-ray computed tomography, a field of high importance primarily due to its central role in medical treatments. This provides insightful interpretations of traditional reconstruction algorithms. Large-scale optimisation is considered in two different contexts. First, we consider a stochastic environment, for which we suggest a new method inspired by the quasi-Newton framework. Promising results are demonstrated on real world benchmark problems. Secondly, we suggest an approach to solve an applied deterministic optimisation problem that arises within the design of electrical circuit boards. We reduce the memory requirements through a tailored algorithm, while also benefiting from other parts of the setting to ensure a high computational efficiency. The final paper scrutinises a publication from the early phase of the COVID-19 pandemic, in which the aim was to assess the effectiveness of different governmental interventions. We show that minor modifications in the input data have important impact on the results, and we argue that great caution is necessary when such models are used as a support for decision making.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Nyckelord

Gaussian processes
Tomographic reconstruction
Large-scale optimisation
Electrical Engineering with specialization in Signal Processing
Elektroteknik med inriktning mot signalbehandling

Publikations- och innehållstyp

vet (ämneskategori)
dok (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy