Phy Taylor Framework

The Phy-Taylor framework integrates physical knowledge into deep neural networks (DNNs) to improve accuracy, robustness, and training efficiency in applications where physical laws govern the system's behavior. Current research focuses on developing novel architectures, such as Physics-compatible neural networks (PhNs), that incorporate Taylor series expansions of physical quantities and mechanisms to enforce physical constraints during training. This approach addresses limitations of purely data-driven DNNs by ensuring model compliance with physical principles, leading to more reliable and interpretable models across diverse fields like speech enhancement and time series prediction. The resulting improvements in model performance and reduced computational costs have significant implications for various scientific and engineering applications.