ADD-PINN adaptively decomposes the spatial domain based on PINN residuals and a shock indicator to improve offline traffic state estimation under the LWR model, outperforming baselines in most sparse-sensor cases while training faster.
Department of Transportation Federal Highway Administration
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Enhanced streaming DMD algorithm using residual bounds and Exact DMD vectors for improved numerical robustness, smaller memory use, and better forecasting.
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Adaptive Domain Decomposition Physics-Informed Neural Networks for Traffic State Estimation with Sparse Sensor Data
ADD-PINN adaptively decomposes the spatial domain based on PINN residuals and a shock indicator to improve offline traffic state estimation under the LWR model, outperforming baselines in most sparse-sensor cases while training faster.
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New Robust Streaming DMD with Forecasting
Enhanced streaming DMD algorithm using residual bounds and Exact DMD vectors for improved numerical robustness, smaller memory use, and better forecasting.