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Podcast: Arxiv Papers
Episode: Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees
Description:
This paper introduces a hybrid approach combining physics-informed neural networks and cylindrical approximation to efficiently solve functional differential equations, addressing computational challenges and improving numerical analysis.
https://arxiv.org/abs//2410.18153
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