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Explore the incorporation of boundary conditions in Physics Informed Neural Networks (PINNs) through this informative 18-minute video lecture. Gain insights into the advanced techniques used to enhance the accuracy and reliability of PINNs by effectively integrating boundary conditions into the neural network architecture. Learn how this approach improves the ability of PINNs to solve complex physical problems while maintaining consistency with known physical laws and constraints at the boundaries of the domain.
