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LPINNs: First-Layer Gated Localization for Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) use one shared representation over the computational domain, which can become difficult to optimize on long domains and for high-order operators. We study a minimal alternative: multiply the first hidden activation of an otherwise unchanged dense PINN by inpu
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