Results 71 to 80 of about 57,267 (303)

Synchronization of dissipative dynamical systems driven by non-Gaussian Lévy noises

open access: yes, 2010
Dynamical systems driven by Gaussian noises have been considered extensively in modeling, simulation, and theory. However, complex systems in engineering and science are often subject to non-Gaussian fluctuations or uncertainties.
Kloeden, Peter E.   +3 more
core   +1 more source

Propriety of Posteriors in Structured Additive Regression Models: Theory and Empirical Evidence [PDF]

open access: yes, 2006
Structured additive regression comprises many semiparametric regression models such as generalized additive (mixed) models, geoadditive models, and hazard regression models within a unified framework.
Kneib, Thomas, Fahrmeir, Ludwig
core   +1 more source

Productivity‐Driven Optimization of Laser Powder Bed Fusion Parameters for IN718 Superalloy: Process Control, Microstructure, and Mechanical Properties

open access: yesAdvanced 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

On Fractional Gaussian Random Fields Simulations

open access: yesJournal of Statistical Software, 2007
To simulate Gaussian fields poses serious numerical problems: storage and computing time. The midpoint displacement method is often used for simulating the fractional Brownian fields because it is fast.
Alexandre Brouste   +2 more
doaj  

The Sequential Generation of Gaussian Random Fields for Applications in the Geospatial Sciences

open access: yesISPRS International Journal of Geo-Information, 2014
This paper presents practical methods for the sequential generation or simulation of a Gaussian two-dimensional random field. The specific realizations typically correspond to geospatial errors or perturbations over a horizontal plane or grid. The errors
John Dolloff, Peter Doucette
doaj   +1 more source

Co-simulation of hydrofacies and piezometric data in the West Thessaly basin, Greece: A geostatistical application using the GeoSim R package

open access: yesApplied Computing and Geosciences, 2023
In the present study, we co-simulate hydrofacies and piezometric data in order to construct geostatistical realizations of underground geology in an area of the West Thessaly basin.
George Valakas   +2 more
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

open access: yesAdvanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang   +2 more
wiley   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

open access: yesAdvanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara   +8 more
wiley   +1 more source

Statistical Modeling of Right-Censored Spatial Data Using Gaussian Random Fields

open access: yesMathematics
Consider a fixed number of clustered areas identified by their geographical coordinates that are monitored for the occurrences of an event such as a pandemic, epidemic, or migration.
Fathima Z. Sainul Abdeen   +2 more
doaj   +1 more source

Gaussian Processes for Data Fulfilling Linear Differential Equations

open access: yesProceedings, 2019
A method to reconstruct fields, source strengths and physical parameters based on Gaussian process regression is presented for the case where data are known to fulfill a given linear differential equation with localized sources.
Christopher G. Albert
doaj   +1 more source

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