[HTML][HTML] Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches
Metal additive manufacturing (AM) presents advantages such as increased complexity for a
lower part cost and part consolidation compared to traditional manufacturing. The multiscale …
lower part cost and part consolidation compared to traditional manufacturing. The multiscale …
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 …
Application of deep transfer learning and uncertainty quantification for process identification in powder bed fusion
Accurate identification and modeling of process maps in additive manufacturing remains a
pertinent challenge. To ensure high quality and reliability of the finished product …
pertinent challenge. To ensure high quality and reliability of the finished product …
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 …
A novel global optimization algorithm and data-mining methods for turbomachinery design
X Li, Y Zhao, Z Liu - Structural and Multidisciplinary Optimization, 2019 - Springer
A new multi-objective, multi-disciplinary global optimization strategy is proposed to address
the high-dimensional, computationally expensive black box problem (HEB) in …
the high-dimensional, computationally expensive black box problem (HEB) in …
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 …
Scalable fully Bayesian Gaussian process modeling and calibration with adaptive sequential Monte Carlo for industrial applications
Gaussian process (GP) regression or kriging has been extensively applied in the
engineering literature for the purposes of building a cheap-to-evaluate surrogate, within the …
engineering literature for the purposes of building a cheap-to-evaluate surrogate, within the …
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 …