Hybrid Method

Hybrid methods represent a powerful approach in various scientific fields, aiming to combine the strengths of different techniques to overcome individual limitations and achieve superior performance. Current research focuses on integrating diverse methodologies, such as pairing traditional and next-generation machine learning architectures (e.g., reservoir computing), synergizing gradient-based and swarm optimization algorithms, or combining data-driven and model-based approaches (e.g., physics-informed machine learning). These hybrid approaches are proving valuable in diverse applications, from improving the accuracy and efficiency of time series forecasting and optimization problems to enhancing the explainability of complex systems and accelerating computationally intensive simulations.