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Researchers propose a Physics-Informed Machine Learning (PIML) framework that integrates hydrological constraints into an LSTM loss function to improve short-term flood forecasting, particularly in data-scarce regimes. A 'Trend Alignment' constraint enforcing consistency between precipitation and discharge trends improves Nash-Sutcliffe Efficiency and eliminates unphysical predictions during extreme events.
SubsurfaceGen is a GPU-accelerated generator for 3D velocity models and seismic data, releasing a dataset of 4,276 2D velocity slices and associated wavefields and shot gathers across six geological settings, aimed at advancing machine learning for full waveform inversion.