/ Extracting Useful Information from Noisy Data Sets Using Singular Value Decomposition and Galerkin-Based Reduced Order Method

Extracting Useful Information from Noisy Data Sets Using Singular Value Decomposition and Galerkin-Based Reduced Order Method

Extracting Useful Information from Noisy Data Sets Using Singular Value Decomposition and Galerkin-Based Reduced Order Method

  • September 23, 2025
  • Category: MCS Weekly Seminars
  • Comments Off on Extracting Useful Information from Noisy Data Sets Using Singular Value Decomposition and Galerkin-Based Reduced Order Method

Title: Extracting Useful Information from Noisy Data Sets Using Singular Value Decomposition and Galerkin-Based Reduced Order Method
Presenter: Mr. Japath Tikil
Date: Thursday, 25 September 2025
Time: 12:00 – 1:00 pm
Venue: MCS203
Summary:
Mr. Tikil will present Chapters 6–9 of his thesis, focusing on extracting useful information from noisy data sets using Singular Value Decomposition (SVD) and Galerkin-based Reduced Order Methods. The presentation will emphasize the theoretical framework, practical applications, and results obtained from his research.