An automated Python simulator, calibrated to one experimental run, generates consistent time-series data for many batch distillation scenarios including anomalies, forming an openly released hybrid dataset for deep anomaly detection.
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DSPR decouples temporal patterns and residual dynamics with physics priors to improve accuracy and plausibility in non-stationary industrial forecasting.
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Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
An automated Python simulator, calibrated to one experimental run, generates consistent time-series data for many batch distillation scenarios including anomalies, forming an openly released hybrid dataset for deep anomaly detection.
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DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting
DSPR decouples temporal patterns and residual dynamics with physics priors to improve accuracy and plausibility in non-stationary industrial forecasting.