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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > BIMSA Digital Economy Lab Seminar The mathematics and control in filtering problem
The mathematics and control in filtering problem
Organizers
Li Yan Han , Zhen Li , Fei Long , Ke Tang , Yu Wang
Speaker
Jia Yi Kang
Time
Friday, February 28, 2025 3:00 PM - 4:00 PM
Venue
A3-2a-302
Online
Zoom 637 734 0280 (BIMSA)
Abstract
In practical estimation applications, observational data from physical models are invariably affected by various forms of noise. In the current data-driven era, extracting and recovering meaningful information has emerged as a critical challenge. Filtering theory, which combines time-series observational data with prior knowledge of physical models, provides a framework for addressing these estimation problems. This theory is widely applicable to sequential tasks in fields such as communications, finance, navigation, image processing, and geophysics. Traditional approaches to solving filtering problems generally rely on two methodological frameworks. The first involves formulating stochastic differential equations through the theory of stochastic partial differential equations, followed by applying analytical theories and numerical algorithms to derive closed-form solutions for specific cases or approximate solutions for broader scenarios. The second employs particle-based techniques, such as Monte Carlo methods, to reconstruct statistical properties of system states or approximate posterior density functions using particle ensembles. Recent advancements in artificial intelligence have enabled data-driven and AI-powered strategies to design more efficient filtering algorithms, opening new avenues for addressing complex industrial and financial challenges in real-world settings.
Beijing Institute of Mathematical Sciences and Applications
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