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 …

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 …

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 …

[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 …

Accelerating additive design with probabilistic machine learning

Y Zhang, S Karnati, S Nag… - … -ASME Journal of …, 2022‏ - asmedigitalcollection.asme.org
Additive manufacturing (AM) has been growing rapidly to transform industrial applications.
However, the fundamental mechanism of AM has not been fully understood which resulted …

Efficient robust design for thermoacoustic instability analysis: A gaussian process approach

S Guo, CF Silva, W Polifke - … of Engineering for …, 2020‏ - asmedigitalcollection.asme.org
In the preliminary phase of analyzing the thermoacoustic characteristics of a gas turbine
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 …

Efficient bayesian inverse method using robust gaussian processes for design under uncertainty

S Ghosh, P Pandita, W Subber, Y Zhang… - AIAA Scitech 2020 …, 2020‏ - arc.aiaa.org
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 …

Efficient robust design for thermoacoustic instability analysis: a Gaussian process approach

S Guo, CF Silva, W Polifke - … Expo: Power for …, 2019‏ - asmedigitalcollection.asme.org
In the preliminary phase of analysing the thermoacoustic characteristics of a gas turbine
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 …