New AI Framework Combines Physics and Data for More Reliable Industrial Forecasting

Researchers introduced DSPR, a dual-stream neural network that balances predictive accuracy with physical plausibility for industrial time series forecasting. The framework decouples statistical patterns from regime-dependent dynamics while incorporating physical constraints, achieving 99%+ accuracy across heterogeneous operating conditions and enabling trustworthy autonomous control systems.