TimesNet-Gen generates station-specific strong motion records from a frozen pre-trained model using Dirichlet-based latent space resampling, achieving cross-regional generalization on NGA-West2 data without fine-tuning.
Benjamin Erichson, and Michael W
2 Pith papers cite this work. Polarity classification is still indexing.
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NeurDE learns the equilibrium closure within a kinetic solver to outperform larger neural models on long-term predictions of nonlinear conservation laws including shocks.
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TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
TimesNet-Gen generates station-specific strong motion records from a frozen pre-trained model using Dirichlet-based latent space resampling, achieving cross-regional generalization on NGA-West2 data without fine-tuning.
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Neural equilibria for long-term prediction of nonlinear conservation laws
NeurDE learns the equilibrium closure within a kinetic solver to outperform larger neural models on long-term predictions of nonlinear conservation laws including shocks.