Spring 2023
Committee: Dr. Adrian Sandu (Chair); Dr. Alexey Onufriev;
Dr. Young Cao
Timeline. Students must commit to take exam and register with the chair of the exam by Dec. 6, 2022. Students may withdraw from taking the exam by Jan. 9, 2023.
The Qualifying Exam has both a written and an oral component.
Written: Select four papers from the reading list below: papers 1) and 2), and two papers of your choice from the set {3), 4), 5)}. For each of the papers you selected write a short (no more than one page) summary and critique of the paper in your own words. Your writeup should identify the main points or contributions of the paper, as well as the significance of the contributions. Highlight any particularly strong or weak aspects of a paper. Submit this written material to Dr. Sandu via email (sandu@cs.vt.edu) by February 1, 2022.
Oral: In February, you will make an oral presentation to the committee on two papers selected by you from the reading list. Prepare roughly 30 minutes of material. Do not provide a tutorial or short course about the details or minutiae in the papers. Your material should:
The committee will ask questions throughout the oral exam not only regarding
the two papers you chose, but on any of the papers in the list (and potential
research directions).
In preparing for this exam, you should do the work yourself and should find and read any other references that seem useful. You may discuss papers in the reading list with other students who are preparing for the exam. However, you are not to work with others in preparing submissions to the exam (e.g., summaries and critiques, the talk, etc.).
1) McLachlan, R., & Quispel, G. (2002). Splitting methods. Acta Numerica, 11, 341-434.
https://doi.org/10.1017/S0962492902000053
2) R. Giering and T. Kaminski. Recipes for adjoint code construction. ACM Transactions on
Mathematical Software, Vol. 24, No. 4, December 1998.
doi:10.1145/293686.293695
3) J. M. Sanz-Serna. Symplectic Runge--Kutta Schemes for Adjoint Equations, Automatic Differentiation, Optimal Control, and More.
SIAM Review, Volume 58, Issue 1, 2016
https://doi.org/10.1137/151002769
4) George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang
Physics-informed machine learning
Nature Reviews Physics volume 3, pages 422–440 (2021)
https://doi.org/10.1038/ s42254-021-00314-5
5) NICK BOSTROM
ARE YOU LIVING IN A COMPUTER SIMULATION?
Philosophical Quarterly (2003) Vol. 53, No. 211, pp. 243‐255.