Model Driven

Model-driven approaches aim to leverage pre-defined models and structures to improve various aspects of software development and data analysis, contrasting with purely data-driven methods. Current research focuses on integrating model-driven techniques with machine learning, particularly using large language models and neural networks for tasks like code generation, probabilistic forecasting, and threat detection. This approach enhances efficiency, accuracy, and controllability in diverse applications, ranging from robotics and autonomous vehicles to cybersecurity and biomedical image analysis. The ultimate goal is to create more robust, reliable, and maintainable systems by combining the strengths of structured modeling with the power of AI.