Hybrid Modeling

Hybrid modeling integrates data-driven machine learning with established physical or theoretical models to leverage the strengths of both approaches. Current research focuses on improving model accuracy, interpretability, and efficiency through various architectures, including neural networks (e.g., RNNs, transformers), and algorithms like double machine learning and techniques for online model calibration. This approach is proving valuable across diverse fields, enhancing prediction accuracy in areas such as weather forecasting, climate modeling, and robotics, while also improving the understanding of complex systems through increased model interpretability.