WaveFlowGMM generates scenario-conditioned three-component ground-motion waveforms by using symbolic learning for PGA amplitude and AlphaFlow for normalized wavelet-packet waveforms that are later rescaled.
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Boore, Jonathan P
2 Pith papers cite this work, alongside 1,717 external citations. Polarity classification is still indexing.
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Urban structural damage shows phase transitions and critical regimes via a random-field Ising model, with common modeling practices biasing risk metrics by up to 50% under moderate earthquakes.
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Scenario-conditioned flow matching for probabilistic generation of three-component ground-motion waveforms
WaveFlowGMM generates scenario-conditioned three-component ground-motion waveforms by using symbolic learning for PGA amplitude and AlphaFlow for normalized wavelet-packet waveforms that are later rescaled.
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Phase Transitions in Collective Damage of Civil Structures under Natural Hazards
Urban structural damage shows phase transitions and critical regimes via a random-field Ising model, with common modeling practices biasing risk metrics by up to 50% under moderate earthquakes.