Hybrid Analysis

Hybrid analysis combines physics-based models with data-driven approaches like machine learning to create more robust and accurate predictive models. Current research focuses on integrating these methods effectively, exploring architectures such as physics-informed neural networks and corrective source term approaches to improve model reliability and generalizability across diverse applications. This interdisciplinary field is significant because it addresses limitations of solely relying on either physics or data, leading to improved predictions in areas ranging from climate modeling and materials science to anomaly detection in complex systems.