Differentiable Programming is an approach to utilizing machine learning algorithms that involves incorporating domain knowledge into the model design. It allows for the combination of physics-based models with data-driven machine learning, resulting in hybrid models that can leverage both approaches to improve performance and reliability. By integrating prior knowledge about a system's behavior into a neural network architecture, we can create more accurate and robust models that require less training time and resources. This technique has wide applications across various domains such as healthcare, energy, and scientific machine learning. Reference(s): [1] Data-Driven Physics