This course covers a selection of fundamental concepts and algorithms for scientific and visual computing. Based on prior knowledge in basis calculus, linear algebra, numerical interpolation and optimization, this course introduces the concept of the phase space, variational principles, methods for ordinary and partial differential equations, Fourier analysis, and multiscale modeling. The lecture is algorithmically oriented, aiming to enable the students to develop efficient solutions for practically relevant problems, based on solid theoretical foundations and mathematically precise modeling. It covers practical applications, like the simulation of rigid and deformable objects, fibers, fluids, molecular dynamics, signal/image analysis and processing, as well as wavelet-based modeling on different scales.
The course will assume basic knowledge such as taught in MATH41, MATH42, CS103, or CS205A. It is suitable for undergraduate as well as for graduate students.
Course materials are provided on http://cs448j.stanford.edu/.
Application: Phase Space Analysis
Application: Systems of Coupled Oscillators, Particle Systems
Application: Variational-based Image Segmentation
Application: Simulation of Deformable Objects
Application: Rigid Body Dynamics
Application: Molecular Dynamics
Application: Fiber Simulation (Cosserat Equations)
Application: Fluid Simulation (Navier-Stokes Equations)
Application: Signal Analysis and Filtering
Application: Image Compression
Application: Hierarchical Spacetime Control
Application: Wavelet Importance Sampling
There will be a problem set assigned each week, which will be graded. This homework track is mostly theoretical, but it will include a final project and smaller programming tasks along the way. The final project will consist of writing a simulator for one of the main types of phenomena discussed in the course. The students may collaborate on the assignments provided each student writes up his or her own solutions and clearly lists the names of all the students in the group. There will be no midterm or final exam.
Assignments: 25%, Final Project: 75%.
The university expects both faculty and students to respect and follow Stanford’s Honor Code.
S. Arora, B. Barak. Computational Complexity: A Modern Approach. Cambridge University Press, 2009.
J.-L. Basdevant. Variational Principles in Physics. Springer, 2007.
I. Daubechies. Ten Lectures on Wavelets. SIAM, 1992.
W. Hackbusch. Multi-Grid Methods and Applications. Springer, 2010.
J.M. Haile. Molecular Dynamics Simulation: Elementary Methods. Wiley, 1997.
E. Hairer, S.P. Nørsett. Solving Ordinary Differential Equations I: Nonstiff Problems. Springer, 2009.
E. Hairer, C. Lubich. Geometric Numerical Integration: Structure-Preserving Algorithms for Ordinary Differential Equations. Springer, 2010.
E. Hairer, G. Wanner. Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems. Springer, 2010.
G. Kaiser. A Friendly Guide to Wavelets. Birkhäuser Classics, 2011.
L.D. Landau, E.M. Lifshitz. Mechanics, Third Edition, Course of Theoretical Physics, Volume 1. Butterworth-Heinemann, 1982.
R.H. Landau, M.J. Páez, C.C. Bordeianu. Computational Physics: Problem Solving with Computers. Wiley, 2007.
A. Mitiche, I.B. Ayed. Variational and Level Set Methods in Image Segmentation. Springer, 2011.
R.M. Murray, Z.Li, S.S. Sastry. A Mathematical Introduction to Robotic Manipulation. CRC Press, 1994.
S. Osher, R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Springer, 2002.
E.M. Stein. Fourier Analysis: An Introduction. Princeton University Press, 2003.
G. Strang. Computational Science and Engineering. Wellesley-Cambridge Press, 2007.
O.C. Zienkiewicz, R.L. Taylor, J.Z. Zhu. The Finite Element Method: Its Basis and Fundamentals. Butterworth-Heinemann, 2013.
Course Assistant: David Hyde
Office: 209 Gates CS Bldg.
Office Hours: F 10-12