<ul><li>Nuclear reactor buildings must be designed to withstand the dynamic load induced by strong ground motion earthquakes.</li><li>In this study, an AI physics-based approach is proposed to generate synthetic ground motion by integrating a neural operator and a denoising diffusion probabilistic model.</li><li>The neural operator approximates the elastodynamics Green's operator, while the diffusion model corrects the generated ground motion time series.</li><li>The approach enhances the realism of synthetic seismograms and improves the frequency biases and Goodness-Of-Fit (GOF) scores.</li></ul>

Integrating Fourier Neural Operators with Diffusion Models to improve Spectral Representation of Synthetic Earthquake Ground Motion Response

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