Dynamic Process

Dynamic process modeling aims to understand and predict the evolution of systems over time, encompassing diverse fields from neural computation to industrial processes. Current research emphasizes developing robust and accurate predictive models, employing architectures like recurrent neural networks, transformers, and physics-informed neural networks, often incorporating techniques like process mining and continual learning to handle complex dynamics and limited data. These advancements are crucial for optimizing various applications, including improving industrial efficiency, enhancing biomedical experiments, and enabling more sophisticated financial modeling. The focus is on balancing model accuracy with interpretability and addressing challenges like long-term dependencies, multi-variate relationships, and the need for efficient data utilization.