[Eoas-seminar] [Seminar-announce] Scientific Computing Colloquium with Hamza Ruzayqat

[eoas-seminar at lists.fsu.edu]

"Stochastic Filtering in High Dimensions"

Hamza Ruzayqat
Computer, Electrical and Mathematical Sciences and Engineering,
King Abdullah University of Science and Technology (KAUST)

Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend.

NOTE: In-person attendance is requested in our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is intended for external (non-departmental) participants only.

https://fsu.zoom.us/j/94273595552
Meeting # 942 7359 5552

🎦 Colloquium recordings will be made available here, https://www.sc.fsu.edu/colloquium<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Ce97313f771f1489803f908dd5281f3ad%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638757440259619985%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=9nwPkQNJY2DWVy4pRcpzg8vl8l8EOnj44rJGTh0vbvE%3D&reserved=0>


Wednesday, Feb 26, 2025, Schedule:

* 3:00 to 3:30 PM Eastern Time (US and Canada)
☕ Nespresso & Teatime - 417 DSL Commons

* 3:30 to 4:30 PM Eastern Time (US and Canada)
🕟 Colloquium - 499 DSL Seminar Room


Abstract:
Data assimilation, also known as stochastic filtering, seeks to learn/estimate the evolving state of a physical system by combining a mathematical model with partial and noisy real observations. This process is formulated as a state-space model (SSM), where the objective is to refine state estimates and enhance future predictions in real-time applications (e.g. weather forecasting). However, filtering high-dimensional, nonlinear SSMs presents significant computational challenges. Particle filters offer an exact solution in theory, but their practical implementation suffers from the curse of dimensionality, requiring an exponentially large number of samples, $N = O(\kappa^d)$, with $\kappa > 1$. This makes them computationally prohibitive. An alternative is to use Markov Chain Monte Carlo (MCMC) sampling to generate realizations from the filtering distribution. However, since this distribution incorporates the entire state history up to the current time, MCMC’s computational complexity increases linearly with time, limiting its efficiency in real-time settings. In this talk, I will introduce two novelfiltering techniques designed to alleviate these computational burdens. The first replaces the standard smoothing distribution with a lagged approximation, effectively reducing dependencies between states. This modification, which integrates particle filtering with sequential Monte Carlo samplers, introduces a small bias that remains controlled in both dimension and time while ensuring a computational cost that grows polynomially with $d$. The second method utilizes sequential MCMC (SMCMC) to sample from a particular approximation of the filtering distribution, which is shown to be asymptotically exact. In many cases, the cost of this new algorithm scales linearly with $d$. Additionally, I will present a localized version of SMCMC that significantly reduces computational cost in cases where the available data is very sparse or highly localized.

Additional colloquium details can be found here,
https://www.sc.fsu.edu/news-and-events/colloquium/1858-colloquium-with-hamza-ruzayqat-2025-02-26<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1858-colloquium-with-hamza-ruzayqat-2025-02-26&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Ce97313f771f1489803f908dd5281f3ad%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638757440259639712%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=XHKI6NO7Z4DvbdDLiUjHPRU7Qbxq9mW5nDxf%2F7kTRlg%3D&reserved=0>

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