Results 111 to 120 of about 7,552,651 (318)

Non-Gaussian Process Dynamical Models

IEEE Open Journal of Signal Processing
Probabilistic dynamical models used in applications in tracking and prediction are typically assumed to be Gaussian noise driven motions since well-known inference algorithms can be applied to these models. However, in many real world examples deviations
Yaman Kindap, Simon Godsill
doaj   +1 more source

A Simplified Laminar Flow Model for the Pultrusion of Glass Fiber/Polyethylene Terephthalate Commingled Yarns

Advanced Engineering Materials, EarlyView.
A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares, Nadège Bouchonneau, Christian Jochum, Donald Radford   +3 more
wiley   +1 more source

Derivative observations in Gaussian Process models of dynamic systems [PDF]

, 2002
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model.
Murray-Smith , R., Leithead, WE, Leith, D, Solak, E., Leithead, W.E., Leithead , W., Rasmussen, C., ,, Rasmussen, C.E., Leith, D., Rasmussen, CE, Murray-Smith, R., Rasmusson, C., Leith, D.J., Solak, E, Murray Smith, R   +15 more
core  

Adaptive, cautious, predictive control with Gaussian process priors [PDF]

, 2003
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon.
Sbarbaro, D., Daniel Sbarbaro, Sbarbaro, CE, Rasmussen, C., ,, Carl Edward Rasmussen, Rasmussen, C.E., Agathe Girard, Murray Smith, RD, Girard, A., Rasmussen, CE, Murray-Smith, R., Roderick Murray-Smith, Girard, A   +13 more
core   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Analytic Moment-based Gaussian Process Filtering [PDF]

, 2009
04.07.13 KB. Ok to add accepted version to Spiral, authors retain copyright.We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes.
Huber, MF, Deisenroth, MP, Hanebeck, UD
core  

On Maximum of Gaussian Non-Centered Fields Indexed on Smooth Manifolds [PDF]

, 2001
The double sum method of evaluation of probabilities of large deviations for Gaussian processes with non-zero expectations is developed. Asymptotic behaviors of the tail of non-centered locally stationary Gaussian fields indexed on smooth manifolds are ...
Piterbarg, Vladimir, Stamatovich, Sinisha, Sinisha Stamatovich, Vladimir Piterbarg   +3 more
core   +1 more source

An Improved Gaussian Sum Extended Kalman Filter With Colored Noise for GNSS/SINS Tightly Coupled Positioning and Attitude Determination Systems

IEEE Access
The Gaussian sum extended Kalman filter (GSEKF), as a nonlinear non-Gaussian filter, disregards the impact of colored noise components in non-Gaussian noise engendered by external interference, which may compromise the estimation accuracy in global ...
Qing Dai, Guorui Xiao, Ru Wan, Shenghui Li   +3 more
doaj   +1 more source

Classical simulation and quantum resource theory of non-Gaussian optics [PDF]

Quantum
We propose efficient algorithms for classically simulating Gaussian unitaries and measurements applied to non-Gaussian initial states. The constructions are based on decomposing the non-Gaussian states into linear combinations of Gaussian states.
Oliver Hahn, Ryuji Takagi, Giulia Ferrini, Hayata Yamasaki   +3 more
doaj   +1 more source

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

Advanced 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, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source