[HTML][HTML] The present and future of QCD
P Achenbach, D Adhikari, A Afanasev, F Afzal… - Nuclear Physics A, 2024 - Elsevier
Abstract This White Paper presents an overview of the current status and future perspective
of QCD research, based on the community inputs and scientific conclusions from the 2022 …
of QCD research, based on the community inputs and scientific conclusions from the 2022 …
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 …
Applications of emulation and Bayesian methods in heavy-ion physics
JF Paquet - Journal of Physics G: Nuclear and Particle Physics, 2024 - iopscience.iop.org
Heavy-ion collisions provide a window into the properties of many-body systems of
deconfined quarks and gluons. Understanding the collective properties of quarks and …
deconfined quarks and gluons. Understanding the collective properties of quarks and …
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 …
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression
Fourier feature approximations have been successfully applied in the literature for scalable
Gaussian Process (GP) regression. In particular, Quadrature Fourier Features (QFF) derived …
Gaussian Process (GP) regression. In particular, Quadrature Fourier Features (QFF) derived …
Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators
A critical bottleneck for scientific progress is the costly nature of computer simulations for
complex systems. Surrogate models provide an appealing solution: such models are trained …
complex systems. Surrogate models provide an appealing solution: such models are trained …
Diverse Expected Improvement (DEI): Diverse Bayesian Optimization of Expensive Computer Simulators
The optimization of expensive black-box simulators arises in a myriad of modern scientific
and engineering applications. Bayesian optimization provides an appealing solution, by …
and engineering applications. Bayesian optimization provides an appealing solution, by …
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy
In an era where scientific experiments can be very costly, multifidelity emulators provide a
useful tool for cost-efficient predictive scientific computing. For scientific applications, the …
useful tool for cost-efficient predictive scientific computing. For scientific applications, the …
A misfire-integrated Gaussian process (MInt-GP) emulator for energy-assisted compression ignition (EACI) engines with varying cetane number jet fuels
SR Narayanan, Y Ji, HD Sapra… - … Journal of Engine …, 2024 - journals.sagepub.com
For energy-assisted compression ignition (EACI) engine propulsion at high-altitude
operating conditions using sustainable jet fuels with varying cetane numbers, it is essential …
operating conditions using sustainable jet fuels with varying cetane numbers, it is essential …
eRPCA: Robust Principal Component Analysis for Exponential Family Distributions
Robust principal component analysis (RPCA) is a widely used method for recovering low‐
rank structure from data matrices corrupted by significant and sparse outliers. These …
rank structure from data matrices corrupted by significant and sparse outliers. These …