A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization

M Binois, N Wycoff - ACM Transactions on Evolutionary Learning and …, 2022 - dl.acm.org
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

A hierarchical expected improvement method for bayesian optimization

Z Chen, S Mak, CFJ Wu - Journal of the American Statistical …, 2024 - Taylor & Francis
Abstract The Expected Improvement (EI) method, proposed by Jones, Schonlau, andWelch,
is a widely used Bayesian optimization method, which makes use of a fitted Gaussian …

AeroVR: An immersive visualisation system for aerospace design and digital twinning in virtual reality

SK Tadeja, P Seshadri, PO Kristensson - The Aeronautical Journal, 2020 - cambridge.org
One of today's most propitious immersive technologies is virtual reality (VR). This term is
colloquially associated with headsets that transport users to a bespoke, built-for-purpose …

Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma

K Li, S Mak, JF Paquet, SA Bass - arXiv preprint arXiv:2306.07299, 2023 - arxiv.org
The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled
the Universe shortly after the Big Bang. A critical challenge in studying the QGP is that, to …

Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery

T Tang, S Mak, D Dunson - SIAM/ASA Journal on Uncertainty Quantification, 2024 - SIAM
In many areas of science and engineering, computer simulations are widely used as proxies
for physical experiments, which can be infeasible or unethical. Such simulations are often …

Bayesian assessments of aeroengine performance with transfer learning

P Seshadri, AB Duncan, G Thorne, G Parks… - Data-Centric …, 2022 - cambridge.org
Aeroengine performance is determined by temperature and pressure profiles along various
axial stations within an engine. Given limited sensor measurements, we require a …

A multi-fidelity approximation of the active subspace method for surrogate models with high-dimensional inputs

B Mufti, M Chen, C Perron, DN Mavris - AIAA AVIATION 2022 Forum, 2022 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2022-3488. vid Modern design problems
routinely involve high-dimensional inputs and the active subspace has been recognized as …

A hierarchical expected improvement method for Bayesian optimization

Z Chen, S Mak, CF Wu - arXiv preprint arXiv:1911.07285, 2019 - arxiv.org
The Expected Improvement (EI) method, proposed by Jones et al.(1998), is a widely-used
Bayesian optimization method, which makes use of a fitted Gaussian process model for …

A fully Bayesian gradient-free supervised dimension reduction method using Gaussian processes

R Gautier, P Pandita, S Ghosh… - International Journal for …, 2022 - dl.begellhouse.com
Modern day engineering problems are ubiquitously characterized by sophisticated computer
codes that map parameters or inputs to an underlying physical process. In other situations …

Design space exploration of stagnation temperature probes via dimension reduction

AD Scillitoe, B Ubald… - … Expo: Power for …, 2020 - asmedigitalcollection.asme.org
The measurement of stagnation temperature is important for turbomachinery applications as
it is used in the calculation of component efficiency and engine specific fuel consumption …