CME 102: Ordinary Differential Equations for Engineers (ENGR 155A)
Analytical and numerical methods for solving ordinary differential equations arising in engineering applications: Solution of initial and boundary value problems, series solutions, Laplace transforms, and nonlinear equations; numerical methods for solving ordinary differential equations, accuracy of numerical methods, linear stability theory, finite differences. Introduction to MATLAB programming as a basic tool kit for computations. Problems from various engineering fields. Prerequisite: 10 units of AP credit (Calc BC with 4 or 5, or Calc AB with 5), or
Math 41 and 42. Recommended:
CME100.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Le, H. (PI)
;
Moin, P. (PI)
;
Chen, L. (TA)
;
Dancoisne, B. (TA)
;
DebaillonVesque, O. (TA)
;
Dupont, E. (TA)
;
Gao, P. (TA)
;
Genin, M. (TA)
;
Harris, S. (TA)
;
Patki, R. (TA)
;
Paudel, S. (TA)
;
Shaikh, S. (TA)
;
Sheshadri, A. (TA)
;
Sunder Raj, A. (TA)
;
Suresha, S. (TA)
;
Usmani, S. (TA)
;
de Lichy, C. (TA)
;
shirian, y. (TA)
CME 102A: Ordinary Differential Equations for Engineers, ACE
Students attend
CME102/ENGR155A lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Prerequisite: students must be enrolled in the regular section (
CME102) prior to submitting application at:
http://soe.stanford.edu/current_students/edp/programs/ace.html
Terms: Aut, Win, Spr

Units: 6

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Le, H. (PI)
;
Moin, P. (PI)
;
Chen, L. (TA)
;
Dancoisne, B. (TA)
;
Dupont, E. (TA)
;
Gao, P. (TA)
;
Genin, M. (TA)
;
Patki, R. (TA)
;
Paudel, S. (TA)
;
Sunder Raj, A. (TA)
;
Suresha, S. (TA)
;
Usmani, S. (TA)
;
de Lichy, C. (TA)
CME 104: Linear Algebra and Partial Differential Equations for Engineers (ENGR 155B)
Linear algebra: matrix operations, systems of algebraic equations, Gaussian elimination, undetermined and overdetermined systems, coupled systems of ordinary differential equations, eigensystem analysis, normal modes. Fourier series with applications, partial differential equations arising in science and engineering, analytical solutions of partial differential equations. Numerical methods for solution of partial differential equations: iterative techniques, stability and convergence, time advancement, implicit methods, von Neumann stability analysis. Examples and applications from various engineering fields. Prerequisite:
CME 102/
ENGR 155A.
Terms: Spr

Units: 5

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Khayms, V. (PI)
;
Gao, P. (TA)
;
Hegde, V. (TA)
;
Katanforoosh, K. (TA)
;
shirian, y. (TA)
CME 104A: Linear Algebra and Partial Differential Equations for Engineers, ACE
Students attend
CME104/ENGR155B lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Prerequisite: students must be enrolled in the regular section (
CME102) prior to submittingapplication at:
http://soe.stanford.edu/current_students/edp/programs/ace.html
Terms: Spr

Units: 6

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Khayms, V. (PI)
;
Gao, P. (TA)
;
Hegde, V. (TA)
;
Katanforoosh, K. (TA)
;
shirian, y. (TA)
CME 151: Introduction to Data Visualization
Bring your data to life with beautiful and interactive visualizations. This course is designed to provide practical experience on combining data science and graphic design to effectively communicate knowledge buried inside complex data. Each lecture will explore a different set of free industrystandard tools, for example d3.js, three.js, ggplots2, and processing; enabling students to think critically about how to architect their own interactive visualization for data exploration, web, presentations, and publications. Geared towards scientists and engineers, and with a particular emphasis on web, this course assumes an advanced background in programming methodology in multiple languages (particularly R and Javascript). Assignments are short and focus on visual experimentation with interesting data sets or the students' own data. Topics: data, visualization, web. Prerequisites: some experience with general programming is required to understand the lectures and assignments.
Terms: Aut, Win

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Deriso, D. (PI)
CME 161: Interactive Data Visualization
Provides practical experience on combining data science and graphic design to effectively communicate knowledge buried inside complex data. Topics: data, visualization and web; will explore different sets of free industrystandard tools, for example d3.js, three.js, and processing.js; enabling students to think critically about how to architect their own interactive visualization for data exploration, web, presentations, and publications. Advanced topics including immersive 3D visualization using Google Cardboard and dynamic visualization using sensors are explored. Assignments are interactive online tutorials that focus on visual experimentation with interesting data sets or the students' own data. Prerequisites: intermediate level programming experience is required to understand the lectures and assignments.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Deriso, D. (PI)
;
Naik, J. (TA)
CME 192: Introduction to MATLAB
This short course runs for the first eight weeks of the quarter and is offered each quarter during the academic year. It is highly recommended for students with no prior programming experience who are expected to use MATLAB in math, science, or engineering courses. It will consist of interactive lectures and applicationbased assignments.nThe goal of the short course is to make students fluent in MATLAB and to provide familiarity with its wide array of features. The course covers an introduction of basic programming concepts, data structures, and control/flow; and an introduction to scientific computing in MATLAB, scripts, functions, visualization, simulation, efficient algorithm implementation, toolboxes, and more.
Terms: Aut, Win, Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Yu, J. (PI)
CME 193: Introduction to Scientific Python
This short course runs for the first four weeks of the quarter. It is recommended for students who are familiar with programming at least at the level of CS106A and want to translate their programming knowledge to Python with the goal of becoming proficient in the scientific computing and data science stack. Lectures will be interactive with a focus on real world applications of scientific computing. Technologies covered include Numpy, SciPy, Pandas, Scikitlearn, and others. Topics will be chosen from Linear Algebra, Optimization, Machine Learning, and Data Science. Prior knowledge of programming will be assumed, and some familiarity with Python is helpful, but not mandatory.
Terms: Aut, Win, Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
de Oliveira, L. (PI)
CME 195: Introduction to R (STATS 195)
This short course runs for the first four weeks of the quarter and is offered in fall and spring. It is recommended for students who want to use R in statistics, science, or engineering courses and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R.
Terms: Aut, Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Michael, H. (PI)
;
Suo, X. (PI)
CME 206: Introduction to Numerical Methods for Engineering (ME 300C)
Numerical methods from a user's point of view. Lagrange interpolation, splines. Integration: trapezoid, Romberg, Gauss, adaptive quadrature; numerical solution of ordinary differential equations: explicit and implicit methods, multistep methods, RungeKutta and predictorcorrector methods, boundary value problems, eigenvalue problems; systems of differential equations, stiffness. Emphasis is on analysis of numerical methods for accuracy, stability, and convergence. Introduction to numerical solutions of partial differential equations; Von Neumann stability analysis; alternating direction implicit methods and nonlinear equations. Prerequisites:
CME 200/
ME 300A,
CME 204/
ME 300B.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
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