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 …
A survey of Bayesian calibration and physics-informed neural networks in scientific modeling
FAC Viana, AK Subramaniyan - Archives of Computational Methods in …, 2021 - Springer
Computer simulations are used to model of complex physical systems. Often, these models
represent the solutions (or at least approximations) to partial differential equations that are …
represent the solutions (or at least approximations) to partial differential equations that are …
Real-time model calibration with deep reinforcement learning
The real-time, and accurate inference of model parameters is of great importance in many
scientific and engineering disciplines that use computational models (such as a digital twin) …
scientific and engineering disciplines that use computational models (such as a digital twin) …
Adjusting a torsional vibration damper model with physics-informed neural networks
In this work, we implement a framework for adjusting the outputs of a torsional vibration
damper (TVD) model to experimental data using physics-informed neural networks. TVDs …
damper (TVD) model to experimental data using physics-informed neural networks. TVDs …
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 …
A surrogate modeling approach for reliability analysis of a multidisciplinary system with spatio-temporal output
Z Hu, S Mahadevan - Structural and Multidisciplinary Optimization, 2017 - Springer
Reliability analysis of a multidisciplinary system is computationally intensive due to the
involvement of multiple disciplinary models and coupling between the individual models …
involvement of multiple disciplinary models and coupling between the individual models …
The effect of grid resolution and reaction models in simulation of a fluidized bed gasifier through nonintrusive uncertainty quantification techniques
To improve quality of numerical models used in simulations of a fluidized bed gasifier at any
scale, the sources of uncertainty in the simulation have to be identified and quantified. There …
scale, the sources of uncertainty in the simulation have to be identified and quantified. There …
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 …