A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
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 …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
A hierarchical expected improvement method for bayesian optimization
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 …
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
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 …
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
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 …
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
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 …
for physical experiments, which can be infeasible or unethical. Such simulations are often …
Bayesian assessments of aeroengine performance with transfer learning
Aeroengine performance is determined by temperature and pressure profiles along various
axial stations within an engine. Given limited sensor measurements, we require a …
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
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 …
routinely involve high-dimensional inputs and the active subspace has been recognized as …
A hierarchical expected improvement method for Bayesian optimization
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 …
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
Modern day engineering problems are ubiquitously characterized by sophisticated computer
codes that map parameters or inputs to an underlying physical process. In other situations …
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 …
it is used in the calculation of component efficiency and engine specific fuel consumption …