RGFlow uses flow-based neural networks to learn bijective real-space RG transformations for the 2D phi^4 theory, identifying a Wilson-Fisher-like critical point and estimating the correlation length exponent.
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FusionCell fuses DeiT-processed layout geometry with graph-transformer netlist topology via topology-guided cross-attention to predict six standard-cell metrics at 0.92% average MAPE on a 19.5k-cell 7nm dataset.
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Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory
RGFlow uses flow-based neural networks to learn bijective real-space RG transformations for the 2D phi^4 theory, identifying a Wilson-Fisher-like critical point and estimating the correlation length exponent.
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FusionCell: Cross-Attentive Fusion of Layout Geometry and Netlist Topology for Standard-Cell Performance Prediction
FusionCell fuses DeiT-processed layout geometry with graph-transformer netlist topology via topology-guided cross-attention to predict six standard-cell metrics at 0.92% average MAPE on a 19.5k-cell 7nm dataset.