Physics-informed AI is an emerging field that combines the principles of physics with artificial intelligence techniques to develop models that can better predict complex physical systems. By incorporating fundamental physical laws and constraints into machine learning algorithms, physics-informed AI can improve the accuracy, robustness, and interpretability of predictions. This approach can also reduce the amount of training data required and enable extrapolation beyond the training data, making it particularly useful for applications in which data is limited or expensive to obtain. Physics-informed AI has the potential to revolutionize fields such as climate modelling, fluid dynamics, and materials science, among others.
Combining data-driven and model-driven approaches is one form of hybrid AI. It has been applied within IDLab in the context of remote water leak detection, chemical plant control systems and design of complex systems such as planar antennae.
