[QA] Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees

from Arxiv Papers Hosted by Igor Melnyk

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 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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[QA] Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees

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