Physics-Informed Neural Networks (PINNs) integrate known physical laws into neural network learning, particularly for solving differential equations. They embed these laws into the network's loss function, guiding the learning process beyond just data fitting.

This integration helps the network predict solutions that are not only data-driven but also align with physical principles, making PINNs especially useful in fields like fluid dynamics and heat transfer. By blending data with established physics, PINNs offer more accurate and robust predictions, especially in data-scarce scenarios.

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