Computationally efficient prediction of statistical variance in the AC losses of multi-stranded windings

2021 ◽  
Author(s):  
Philip Mellor ◽  
Joshua Hoole ◽  
Nick Simpson
Keyword(s):  
Ac Losses ◽  
Computationally Efficient ◽  
Efficient Prediction ◽  
Statistical Variance

scholarly journals Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach

RSC Advances ◽  
2020 ◽  
Vol 10 (40) ◽  
pp. 23834-23841
Author(s):  
Zong-Rong Ye ◽  
I.-Shou Huang ◽  
Yu-Te Chan ◽  
Zhong-Ji Li ◽  
Chen-Cheng Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Molecules ◽  
Fluorescence Emission ◽  
Emission Wavelength ◽  
Learning Approach ◽  
Computationally Efficient ◽  
Machine Learning Approach ◽  
Efficient Prediction

The combinatorial QSAR and machine learning approach provides the qualitative and computationally efficient prediction for fluorescence emission wavelength of organic molecules.

scholarly journals Revealing architectural order with quantitative label-free imaging and deep learning

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Syuan-Ming Guo ◽  
Li-Hao Yeh ◽  
Jenny Folkesson ◽  
Ivan E Ivanov ◽  
Anitha P Krishnan ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Brain Regions ◽  
Three Dimensions ◽  
Live Cells ◽  
Label Free ◽  
Computationally Efficient ◽  
Human Brain Tissue ◽  
Tissue Slices ◽  
Efficient Prediction ◽  
Fluorescence Images

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.

scholarly journals Efficient modelling of delamination growth using adaptive isogeometric continuum shell elements

2019 ◽  
Vol 65 (1) ◽  
pp. 99-117 ◽  
Author(s):  
Camiel Adams ◽  
Martin Fagerström ◽  
Joris J. C. Remmers
Keyword(s):  
Isogeometric Analysis ◽  
Shell Element ◽  
Lead Times ◽  
Computationally Efficient ◽  
Shell Elements ◽  
Delamination Growth ◽  
Modelling Framework ◽  
Solution Methods ◽  
Efficient Prediction ◽  
Efficient Approximation

Abstract The computational efficiency of CAE tools for analysing failure progression in large layered composites is key. In particular, efficient approximation and solution methods for delamination modelling are crucial to meet today’s requirements on virtual development lead times. For that purpose, we present here an adaptive continuum shell element based on the isogeometric analysis framework, suitable for the modelling of arbitrary delamination growth. To achieve an efficient procedure, we utilise that, in isogeometric analysis, the continuity of the approximation field easily can be adapted via so-called knot insertion. As a result, the current continuum shell provides a basis for an accurate but also computationally efficient prediction of delamination growth in laminated composites. Results show that the adaptive modelling framework works well and that, in comparison to a fully resolved model, the adaptive approach gives significant time savings even for simple analyses where major parts of the domain exhibit delamination growth.

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