Results 111 to 120 of about 346,567 (317)
Nonlinear adaptive control using non-parametric Gaussian Process prior models [PDF]
, 2002 Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model ...
Sbarbaro, D. +3 more
core +1 more source
Advanced Engineering Materials, EarlyView.This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky +5 more
wiley +1 more source
Energies, 2020 Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data ...
Dongmei Zhang +4 more
doaj +1 more source
Learning Conductance: Gaussian Process Regression for Molecular Electronics
, 2022 Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values.
Michael, Deffner +6 more
core +1 more source
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Sensors, 2020 Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic ...
Vu Ngoc Tuan +4 more
doaj +1 more source
Near‐Field Electrospinning Micro‐Printhead Achieves Precise Control of Nanofiber Deposition
Advanced Engineering Materials, EarlyView.A micro‐printhead for near‐field electrospinning enables reproducible deposition of polymer nanofibers with diameters below 50 nm. Systematic parameter studies uncover the mechanisms linking operating conditions to fiber morphology, paving the way for precise and low‐cost nanoscale 3D manufacturing.As a high‐resolution, cost‐effective, and rapid ...
Han Xu, Dario Mager, Jan G. Korvink
wiley +1 more source
Using INLA to fit a complex point process model with temporally varying effects – a case study [PDF]
, 2012 Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses
Soerbye, S +7 more
core
Advanced Engineering Materials, EarlyView.This study demonstrates how optimizing laser power, scanning speed, and hatching distance in laser powder bed fusion can boost the productivity of Inconel 718 manufacturing by up to 29% while maintaining mechanical integrity. The work delivers a validated process window and cost–time analysis, offering industry‐ready guidelines for efficient additive ...
Amir Behjat +7 more
wiley +1 more source
Crowdsourcing Spatial Phenomena Using Trust-Based Heteroskedastic Gaussian Processes
, 2013 Many crowdsourcing applications require spatial modelling of data to make sense of location-based observations provided by multiple users. In this context, We propose a new spatial function modelling approach to address the problem of fusing multiple ...
Jennings, N. R. +6 more
core +1 more source