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Physics-Informed Neural Networks (PINN) are algorithms from deep learning leveraging physical laws by including partial differential equations together with a respective set of boundary and initial conditions as penalty terms into their loss function.
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When and why PINNs fail to train: A neural tangent kernel perspective
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Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks
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Kim, Y., Choi, Y., Widemann, D., and Zohdi, T · 2009
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Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
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