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Deep material network
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Novel machine learning approach based on network structure and mechanistic building block
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Materials: rubber composite, polycrystalline materials, CFRP (e.g. UD and woven composite)
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Small-strain and finite-strain formulations in 2D&3D
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Advantages
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Avoiding extensive offline sampling stage
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Eliminating the need for extra calibration and micromechanical assumptions
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Efficient online predictions without the danger of extrapolation
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Arbitrary material laws in online prediction stage
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Linear computational complexity to the number of degrees of freedom.
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Application​s
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Topology learning of RVE​
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Seamless structure-property relationship and material design
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Scale linking via direct network concatenation (e.g. three-scale CFRP)
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Self-consistent clustering analysis
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Mechanistic RVE model reduction based on clustering technique and micromechanics theory
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Materials: Particle-reinforced composite, polycrystalline materials
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Small-strain and finite-strain formulations in 2D&3D
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Advantages
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Avoiding extensive offline sampling stage
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Explicit mapping between clusters and RVE parts
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Applications​​
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Concurrent multiscale simulation ( integrated with LS-DYNA)
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