Short Course: Uncertainty Quantification and Inverse problems

      Release Date:Nov 27, 2019      Click:次     Audit:航空学院

Short Course: Uncertainty Quantification and Inverse problems

Location: A817, School of Aeronautics

Time: 27th, Nov, 9:00-12:00 14:30-17:30

Reporter: Richard P. Dwight from TU Delft

Invited by: Prof. Zhonghua Han

Sponsored by: The overseas Expertise Introduction Center for Discipline Innovation on Complex Flow and Its Control (the 111 Center), School of Aeronautics

Abstract: This course is a short introduction to the field of uncertainty quantification, with emphasis on techniques for model calibration and large-scale inverse problems with the Bayesian framework.  Lectures are complemented by 3 tutorial exercises in Python, which exploit the theory learned. Prerequisites are: (1) a first-course on probability/statistics (though this material will be briefly refreshed in the first lecture), (2) experience with Python (and numpy) programming would be very beneficial for the tutorials. If 1) is missing I recommend reading Chapter 4 of the textbook Ralph C. Smith "Uncertainty Quantification" in advance of the course.





27th, Nov


Lecture 1: Introduction to UQ and its relevance in aerospace


Lecture 2: Bayesian approaches to regression problems


Lecture 3: Large-scale stochastic inverse problems with high-dimensional randomness (and random fields)




Introduction of Prof. Dwight:

Professor Richard P. Dwight received his Ph.D. from the University of Manchester in 2006, worked as an associate professor in the Department of aerodynamics at the school of Aerospace Engineering, Delft University of technology since 2009, and as a visiting professor at the Centrum voor Wiskunde en Informatica (CWI) in The Netherlands since 2017. Professor Richard P. Dwight has made great achievements in computational fluid dynamics, machine learning, aerodynamic design optimization, surrogate-based optimization method, adjoint method, uncertain optimization method and other fields.


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