Physics-integrated segmented Gaussian process (SegGP) learning for cost-efficient training of diesel engine control system with low cetane numbers
View Video Presentation: https://doi. org/10.2514/6.2023-1283. vid Control model training is
an essential step towards the development of an engine controls system. A robust controls …
an essential step towards the development of an engine controls system. A robust controls …
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 …
Efficient emulation of relativistic heavy ion collisions with transfer learning
Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider
(RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on …
(RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on …
APIK: Active physics-informed kriging model with partial differential equations
Kriging (or Gaussian process regression) becomes a popular machine learning method for
its flexibility and closed-form prediction expressions. However, one of the key challenges in …
its flexibility and closed-form prediction expressions. However, one of the key challenges in …
Federated multi-output Gaussian processes
S Chung, R Al Kontar - Technometrics, 2024 - Taylor & Francis
Multi-output Gaussian process (MGP) regression plays an important role in the integrative
analysis of different but interrelated systems/units. Existing MGP approaches assume that …
analysis of different but interrelated systems/units. Existing MGP approaches assume that …
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 …
Gaussian process subspace prediction for model reduction
Subspace-valued functions arise in a wide range of problems, including parametric reduced
order modeling (PROM), parameter reduction, and subspace tracking. In PROM, each …
order modeling (PROM), parameter reduction, and subspace tracking. In PROM, each …
Conglomerate multi-fidelity Gaussian process modeling, with application to heavy-ion collisions
In an era where scientific experimentation is often costly, multi-fidelity emulation provides a
powerful tool for predictive scientific computing. While there has been notable work on multi …
powerful tool for predictive scientific computing. While there has been notable work on multi …
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 …