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Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective
KS Aggour, VK Gupta, D Ruscitto, L Ajdelsztajn… - MRS …, 2019 - cambridge.org
At GE Research, we are combining “physics” with artificial intelligence and machine learning
to advance manufacturing design, processing, and inspection, turning innovative …
to advance manufacturing design, processing, and inspection, turning innovative …
Advances in bayesian probabilistic modeling for industrial applications
S Ghosh, P Pandita, S Atkinson… - … -ASME Journal of …, 2020 - asmedigitalcollection.asme.org
Industrial applications frequently pose a notorious challenge for state-of-the-art methods in
the contexts of optimization, designing experiments and modeling unknown physical …
the contexts of optimization, designing experiments and modeling unknown physical …
Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model
Y MejiaCruz, JM Caicedo, Z Jiang, JM Franco - Engineering structures, 2023 - Elsevier
Methods for identifying human activity have a wide range of potential applications, including
security, event time detection, intelligent building environments, and human health. Current …
security, event time detection, intelligent building environments, and human health. Current …
[HTML][HTML] Industrial applications of intelligent adaptive sampling methods for multi-objective optimization
J Kristensen, W Subber, Y Zhang, S Ghosh… - Design and …, 2019 - intechopen.com
Multi-objective optimization is an essential component of nearly all engineering design.
However, for industrial applications, the design process typically demands running …
However, for industrial applications, the design process typically demands running …
Accelerating additive design with probabilistic machine learning
Additive manufacturing (AM) has been growing rapidly to transform industrial applications.
However, the fundamental mechanism of AM has not been fully understood which resulted …
However, the fundamental mechanism of AM has not been fully understood which resulted …
Efficient robust design for thermoacoustic instability analysis: A gaussian process approach
In the preliminary phase of analyzing the thermoacoustic characteristics of a gas turbine
combustor, implementing robust design principles is essential to minimize detrimental …
combustor, implementing robust design principles is essential to minimize detrimental …
Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation
M Ellison, FA DiazDelaO, NZ Ince, M Willetts - Applied Mathematical …, 2021 - Elsevier
Computationally expensive models are increasingly employed in the design process of
engineering products and systems. Robust design in particular aims to obtain designs that …
engineering products and systems. Robust design in particular aims to obtain designs that …
Efficient bayesian inverse method using robust gaussian processes for design under uncertainty
Inverse problems pose a painfully complex task when the forward model is a
computationally expensive noisy black-box. This not only limits the number of times the …
computationally expensive noisy black-box. This not only limits the number of times the …
Efficient robust design for thermoacoustic instability analysis: a Gaussian process approach
In the preliminary phase of analysing the thermoacoustic characteristics of a gas turbine
combustor, implementing robust design principles is essential to minimize detrimental …
combustor, implementing robust design principles is essential to minimize detrimental …
Assessing the effect of hydrodynamic parameter uncertainty on AUV performance with gaussian processes
JT Kleiber, LM Miller, S Krauss… - OCEANS 2021: San …, 2021 - ieeexplore.ieee.org
In this paper we present an approach for representing the relationship between
hydrodynamic modeling uncertainties of an autonomous underwater vehicle (AUV) and the …
hydrodynamic modeling uncertainties of an autonomous underwater vehicle (AUV) and the …