Results 101 to 110 of about 346,567 (317)
Probabilistic prediction of permeability damage in waterflooding using gaussian process regression
International Journal of Applied Mechanics and EngineeringWaterflooding represents one of the most extensively employed techniques for secondary oil recovery, where water is injected into reservoirs to displace oil toward production wells and enhance hydrocarbon recovery.
Saifi Redha +4 more
doaj +1 more source
Associations of Rheumatoid Arthritis Disease Activity With Frailty Over Five Years of Follow‐up
Arthritis Care &Research, EarlyView.Objective To evaluate whether rheumatoid arthritis (RA) disease activity is associated with frailty both in cross‐section and longitudinally. Methods Participants within the Veterans Affairs Rheumatoid Arthritis (VARA) registry enrolled from 2003 to 2022 were included.
Courtney N. Loecker +14 more
wiley +1 more source
CoRRGaussian processes (GPs) are powerful and widely used probabilistic regression models, but their effectiveness in practice is often limited by the choice of kernel function. This kernel function is typically handcrafted from a small set of standard functions, a process that requires expert knowledge, results in limited adaptivity to data, and imposes ...
Jihao Andreas Lin +5 more
openaire +2 more sources
Gaussian process dynamic programming [PDF]
Neurocomputing, 2009 Reinforcement learning (RL) and optimal control of systems with continuous states and actions require approximation techniques in most interesting cases. In this article, we introduce Gaussian process dynamic programming (GPDP), an approximate value function-based RL algorithm.
Marc Peter Deisenroth +2 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Polynomial Chaos Expanded Gaussian Process
Machine Learning and Knowledge ExtractionIn complex and unknown processes, global models are fitted over the entire input domain but often tend to perform poorly whenever the response surface exhibits non-stationary behavior and varying smoothness.
Dominik Polke +3 more
doaj +1 more source
Distributed Gaussian Processes
, 2015 Copyright © 2015 by the author(s).To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or ...
Deisenroth, MP, Ng, JW
openaire +5 more sources
Nonparametric identification of linearizations and uncertainty using Gaussian process models – application to robust wheel slip control [PDF]
, 2005 Gaussian process prior models offer a nonparametric approach to modelling unknown nonlinear systems from experimental data. These are flexible models which automatically adapt their model complexity to the available data, and which give not only mean ...
Hansen, J. +2 more
core
Classification of protein interaction sentences via gaussian processes [PDF]
, 2009 The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions.
Polajnar, T. +5 more
core +1 more source
Advanced Engineering Materials, EarlyView.This study explores the lightweight potential of laser additive‐manufactured NiTi triply periodic minimal surface sheet lattices. It systematically investigates the effects of relative density and unit cell size on surface quality, deformation recovery, compression behavior, and energy absorption.
Haoming Mo +3 more
wiley +1 more source