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<b><i>"WoS-NN: Collaborating Walk-on-Spheres with Machine Learning to Solve Elliptic PDEs"</i></b></div>
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<b>Silei Song</b></div>
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Graduate Assistant in Teaching,</div>
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Department of Computer Science,</div>
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Florida State University (FSU)</div>
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Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic.
<b>All are welcome to attend. </b></div>
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<b>NOTE: In-person attendance is requested in our 499 Dirac Science Library (DSL) Seminar Room.
</b>Zoom access is intended for external (non-departmental) participants only. </div>
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<b>https://fsu.zoom.us/j/94273595552 </b></div>
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Meeting # <b>942 7359 5552 </b></div>
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🎦 Colloquium recordings will be made available here,</div>
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<b>Wednesday, Mar 26, 2025, Schedule: </b></div>
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* 3:00 to 3:30 PM Eastern Time (US and Canada) </div>
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☕ Nespresso & Teatime - 417 DSL Commons </div>
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<b>* 3:30 to 4:30 PM Eastern Time (US and Canada) </b></div>
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<b>🕟 Colloquium - 499 DSL Seminar Room </b></div>
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<b>Abstract: </b></div>
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Solving elliptic partial differential equations (PDEs) is a fundamental step in various scientific and engineering studies. As a classic stochastic solver, the Walk on Spheres (WoS) method is a well-established and efficient algorithm that provides accurate
local estimates for PDEs. However, limited by the curse of dimensionality, WoS may not offer sufficiently precise global estimations, which becomes more serious in high-dimensional scenarios. Recent developments in machine learning offer promising strategies
to address this limitation. By integrating machine learning techniques with WoS and space discretization approaches, we developed a novel stochastic solver, WoS-NN. This new method solves elliptic problems with Dirichlet boundary conditions, facilitating precise
and rapid global solutions and gradient approximations. A typical experimental result demonstrated that the proposed WoS-NN method provides accurate field estimations, reducing 76.32% errors while using only 8% of path samples compared to the conventional
WoS method, which saves abundant computational time and resource consumption. This seminar will include a general description of stochastic solvers for different PDEs, the design and execution of WoS-NN, and experiment results indicating the fascination of
our method.</div>
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<b>Additional colloquium details can be found here,</b></div>
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<a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1866-colloquium-with-silei-song-2025-03-26&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Ca13335ba4fea46094bee08dd6af8c19b%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638784338804099772%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=NR1a3jdUDLxCoK99xwk1Z84OhwKvGwA4bYOAXlcGyMM%3D&reserved=0" originalsrc="https://www.sc.fsu.edu/news-and-events/colloquium/1866-colloquium-with-silei-song-2025-03-26" id="OWA7a95c7dd-78e2-b367-1a1c-262e1e3c8cee" class="OWAAutoLink">https://www.sc.fsu.edu/news-and-events/colloquium/1866-colloquium-with-silei-song-2025-03-26</a></div>
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