Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective
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
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 …
On Uncertainty Quantification in Materials Modeling and Discovery: Applications of GE's BHM and IDACE
View Video Presentation: https://doi. org/10.2514/6.2023-0528. vid The coupling of artificial
intelligence and materials characterizations has been a center piece of almost all materials …
intelligence and materials characterizations has been a center piece of almost all materials …
General-surrogate adaptive sampling using interquartile range for design space exploration
A surrogate model is a common tool to approximate system response at untested points for
design space exploration. Adaptive sampling has been studied for improving the accuracy of …
design space exploration. Adaptive sampling has been studied for improving the accuracy of …
A strategy for adaptive sampling of multi-fidelity gaussian processes to reduce predictive uncertainty
Multi-fidelity Gaussian process (GP) modeling is a common approach to employ in resource-
expensive computationally demanding algorithms such as optimization, calibration and …
expensive computationally demanding algorithms such as optimization, calibration and …
Industrial applications of intelligent adaptive sampling methods for multi-objective optimization
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 …
A gaussian process modeling approach for fast robust design with uncertain inputs
KM Ryan, J Kristensen, Y Ling… - … Expo: Power for …, 2018 - asmedigitalcollection.asme.org
Many engineering design and industrial manufacturing applications are tasked with finding
optimum designs while dealing with uncertainty in the design parameters. The performance …
optimum designs while dealing with uncertainty in the design parameters. The performance …
Pro-ML IDeAS: A probabilistic framework for explicit inverse design using invertible neural network
View Video Presentation: https://doi. org/10.2514/6.2021-0465. vid An inverse design
process has the potential to positively impact the difficulties of the traditional iterative …
process has the potential to positively impact the difficulties of the traditional iterative …
Efficient sampling algorithm for electric machine design calculations incorporating empirical knowledge
M Heroth, HC Schmid… - … Conference on Electrical …, 2022 - ieeexplore.ieee.org
In order to meet the increasing demand for electric vehicles, automotive suppliers such as
ZF Friedrichshafen AG are trying to develop modular electric motor platforms. In order to find …
ZF Friedrichshafen AG are trying to develop modular electric motor platforms. In order to find …
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 …