CME 200: Linear Algebra with Application to Engineering Computations (ME 300A)
Computer based solution of systems of algebraic equations obtained from engineering problems and eigensystem analysis, Gaussian elimination, effect of roundoff error, operation counts, banded matrices arising from discretization of differential equations, illconditioned matrices, matrix theory, least square solution of unsolvable systems, solution of nonlinear algebraic equations, eigenvalues and eigenvectors, similar matrices, unitary and Hermitian matrices, positive definiteness, CayleyHamilton theory and function of a matrix and iterative methods. Prerequisite: familiarity with computer programming, and
MATH51.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Gerritsen, M. (PI)
;
Ganguli, S. (TA)
;
Gnanasekaran, A. (TA)
...
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Instructors:
Gerritsen, M. (PI)
;
Ganguli, S. (TA)
;
Gnanasekaran, A. (TA)
;
Lyman, L. (TA)
;
Tazhimbetov, N. (TA)
CME 207: Numerical Methods in Engineering and Applied Sciences (AA 214A, GEOPHYS 217)
Scientific computing and numerical analysis for physical sciences and engineering. Advanced version of CME206 that, apart from CME206 material, includes nonlinear PDEs, multidimensional interpolation and integration and an extended discussion of stability for initial boundary value problems. Recommended for students who have some prior numerical analysis experience. Topics include: 1D and multiD interpolation, numerical integration in 1D and multiD including adaptive quadrature, numerical solutions of ordinary differential equations (ODEs) including stability, numerical solutions of 1D and multiD linear and nonlinear partial differential equations (PDEs) including concepts of stability and accuracy. Prerequisites: linear algebra, introductory numerical analysis (
CME 108 or equivalent).
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 211: Software Development for Scientists and Engineers (EARTH 211)
Basic usage of the Python and C/C++ programming languages are introduced and used to solve representative computational problems from various science and engineering disciplines. Software design principles including time and space complexity analysis, data structures, objectoriented design, decomposition, encapsulation, and modularity are emphasized. Usage of campus wide Linux compute resources: login, file system navigation, editing files, compiling and linking, file transfer, etc. Versioning and revision control, software build utilities, and the LaTeX typesetting software are introduced and used to help complete programming assignments. Prerequisite: introductory programming course equivalent to
CS 106A or instructor consent.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Peles, S. (PI)
;
Santucci, A. (PI)
;
Chen, K. (TA)
;
Habot, N. (TA)
;
Subbiah, V. (TA)
;
Tsaptsinos, A. (TA)
CME 214: Software Design in Modern Fortran for Scientists and Engineers (EARTH 214)
This course introduces software design and development in modern Fortran. Course covers the functional, objectoriented, and parallel programming features introduced in the Fortran 95, 2003, and 2008 standards, respectively, in the context of numerical approximations to ordinary and partial differential equations; introduces objectoriented design and design schematics based on the Unified Modeling Language (UML) structure, behavior, and interaction diagrams; cover the basic use of several opensource tools for software building, testing, documentation generation, and revision control. Recommended: Familiarity with programming in Fortran 90, basic numerical analysis and linear algebra, or instructor approval
Terms: alternate years, given next year

Units: 3

Grading: Letter (ABCD/NP)
CME 244: Project Course in Mathematical and Computational Finance
For graduate students in the MCF track; students will work individually or in groups on research projects.
Terms: Aut

Units: 16

Grading: Letter (ABCD/NP)
CME 262: Imaging with Incomplete Information (CEE 362G)
Statistical and computational methods for inferring images from incomplete data. Bayesian inference methods are used to combine data and quantify uncertainty in the estimate. Fast linear algebra tools are used to solve problems with many pixels and many observations. Applications from several fields but mainly in earth sciences. Prerequisites: Linear algebra and probability theory.
Terms: Aut

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Kitanidis, P. (PI)
CME 263: Introduction to Linear Dynamical Systems (EE 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: leastsquares approximations of overdetermined equations, and leastnorm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singularvalue decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multiinput/multioutput systems, impulse and step matrices; convolution and transfermatrix descriptions. Control, reachability, and state transfer; observability and leastsquares state estimation. Prerequisites: Linear algebra and matrices as in
EE 103 or
MATH 104; ordinary differential equations and Laplace transforms as in
EE 102B or
CME 102.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Aboumrad, G. (TA)
...
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Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Aboumrad, G. (TA)
;
Chemparathy, A. (TA)
;
Momeni, A. (TA)
;
Shah, K. (TA)
;
Zhou, Z. (TA)
CME 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CS 279)
Computational techniques for investigating and designing the threedimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellularlevel simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (
CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Dror, R. (PI)
;
Bedi, R. (TA)
;
ElGabalawy, O. (TA)
...
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Instructors:
Dror, R. (PI)
;
Bedi, R. (TA)
;
ElGabalawy, O. (TA)
;
Fernandes, D. (TA)
;
Paggi, J. (TA)
;
Sanborn, A. (TA)
CME 291: Master's Research
Students require faculty sponsor. (Staff)
Terms: Aut, Win, Spr, Sum

Units: 16

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
Begenau, J. (PI)
;
Biondi, B. (PI)
;
Bosagh Zadeh, R. (PI)
...
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Instructors:
Begenau, J. (PI)
;
Biondi, B. (PI)
;
Bosagh Zadeh, R. (PI)
;
Boyd, S. (PI)
;
Carlsson, G. (PI)
;
Darve, E. (PI)
;
Farhat, C. (PI)
;
Gerritsen, M. (PI)
;
Giesecke, K. (PI)
;
Glynn, P. (PI)
;
Gous, A. (PI)
;
Grundfest, J. (PI)
;
Guibas, L. (PI)
;
Kahn, S. (PI)
;
Lai, T. (PI)
;
Leskovec, J. (PI)
;
Ng, A. (PI)
;
Papanicolaou, G. (PI)
;
Pelger, M. (PI)
;
Rajagopal, R. (PI)
;
Re, C. (PI)
;
Schwartzman, A. (PI)
;
Suckale, J. (PI)
;
Ying, L. (PI)
CME 292: Advanced MATLAB for Scientific Computing
Short course running first four weeks of the quarter (8 lectures) with interactive online lectures and application based assignment. Students will access the lectures and assignments on
https://suclass.stanford.edu. Students will be introduced to advanced MATLAB features, syntaxes, and toolboxes not traditionally found in introductory courses. Material will be reinforced with inclass examples, demos, and homework assignment involving topics from scientific computing. MATLAB topics will be drawn from: advanced graphics (2D/3D plotting, graphics handles, publication quality graphics, animation), MATLAB tools (debugger, profiler), code optimization (vectorization, memory management), objectoriented programming, compiled MATLAB (MEX files and MATLAB coder), interfacing with external programs, toolboxes (optimization, parallel computing, symbolic math, PDEs). Scientific computing topics will include: numerical linear algebra, numerical optimization, ODEs, and PDEs.
Terms: Aut, Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Maddix, D. (PI)
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