METHOD AND APPARATUS FOR RECONSTRUCTING 3D MICROSTRUCTURE USING NEURAL NETWORK
First Claim
1. A method of generating a 3D microstructure using a neural network, the method comprising:
- configuring an initial 3D microstructure;
obtaining a plurality of cross-sectional images by disassembling the initial 3D microstructure in at least one direction of the initial 3D microstructure;
generating first output feature maps with respect to at least one layer of the neural network based on each of the plurality of cross-sectional images to the neural network;
generating second output feature maps with respect to the at least one layer in the neural network based on a 2D original image;
generating a 3D gradient by applying a loss value based on the first output feature maps and the second output feature maps to a back-propagation algorithm in the neural network; and
generating a final 3D microstructure by modifying the initial 3D microstructure based on the 3D gradient.
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Accused Products
Abstract
A method of generating a 3D microstructure using a neural network includes configuring an initial 3D microstructure; obtaining a plurality of cross-sectional images by disassembling the initial 3D microstructure in at least one direction of the initial 3D microstructure; obtaining first output feature maps with respect to at least one layer that constitutes the neural network by inputting each of the cross-sectional images to the neural network; obtaining second output feature maps with respect to at least one layer by inputting a 2D original image to the neural network; generating a 3D gradient by applying a loss value to a back-propagation algorithm after calculating the loss value by comparing the first output feature maps with the second output feature maps; and generating a final 3D microstructure based on the 2D original image by reflecting the 3D gradient to the initial 3D microstructure.
2 Citations
17 Claims
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1. A method of generating a 3D microstructure using a neural network, the method comprising:
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configuring an initial 3D microstructure; obtaining a plurality of cross-sectional images by disassembling the initial 3D microstructure in at least one direction of the initial 3D microstructure; generating first output feature maps with respect to at least one layer of the neural network based on each of the plurality of cross-sectional images to the neural network; generating second output feature maps with respect to the at least one layer in the neural network based on a 2D original image; generating a 3D gradient by applying a loss value based on the first output feature maps and the second output feature maps to a back-propagation algorithm in the neural network; and generating a final 3D microstructure by modifying the initial 3D microstructure based on the 3D gradient. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An apparatus configured to generate a 3D microstructure using a neural network, the apparatus comprising:
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a memory configured to store at least one program; and at least one processor configured to process data in the neural network by executing the at least one program, wherein the at least one processor is configured to; configure an initial 3D microstructure; obtain a plurality of cross-sectional images by disassembling the initial 3D microstructure in at least one direction; generate first output feature maps with respect to at least one layer of the neural network based on each of the plurality of cross-sectional images to the neural network; generate second output feature maps with respect to the at least one layer in the neural network based on a 2D original image; generate a 3D gradient by applying a loss value based on the first output feature maps with the second output feature maps to a back-propagation algorithm in the neural network; and generate a final 3D microstructure by modifying the initial 3D microstructure based on the 3D gradient. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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Specification