Adaptive Deep Learning with Batch Feature Re-Engineering and Differential Dynamical Systems

This paper presents a novel deep learning frame-work that integrates class information directly into batch feature engineering to enhance model interpretability and adaptability. Unlike conventional machine learning models, this approach introduces an innovative training paradigm and evaluation methodology, enabling the model to adapt seamlessly to new data distributions without requiring re-training. By coupling the power of differential equation dynamics simulation, the proposed framework bridges the gap between data-driven learning and mechanistic modeling, offering robust predictive capabilities. A case study on infectious disease modeling demonstrates the effectiveness of this approach, showcasing superior performance compared to traditional methods in handling evolving data distributions and capturing complex system dynamics. This work lays the foundation for adaptive modeling in dynamic, real-world scenarios.