Hybrid Model

Hybrid models combine the strengths of distinct modeling approaches, such as mechanistic models and machine learning algorithms, to improve prediction accuracy, interpretability, and efficiency. Current research focuses on integrating various architectures, including neural networks (CNNs, RNNs, Transformers, Graph Neural Networks), with classical methods like optimization algorithms or differential equations, across diverse applications from finance and robotics to weather prediction and biomedical imaging. This interdisciplinary approach is proving valuable for tackling complex problems where a single modeling technique falls short, leading to advancements in various scientific fields and improved performance in real-world applications.