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Deep Learning in Deterministic Computational Mechanics
The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature.
Transfer Learning Enhanced Full Waveform Inversion
We propose a way to favorably employ neural networks in the field of non-destructive testing using Full Waveform Inversion (FWI).
Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion
In the training process of this method, an FCN establishes a non-linear mapping from measured ultrasonic data to the corresponding velocity models of multi-layered bonded composites.
Image-based numerical characterization and experimental validation of tensile behavior of octet-truss lattice structures
The production of lightweight metal lattice structures has received much attention due to the recent developments in additive manufacturing (AM).
Computed Tomography (CT)
Computational Engineering, Finance, and Science
