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Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees
Manage episode 446869474 series 3524393
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1631 bölüm
Manage episode 446869474 series 3524393
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1631 bölüm
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