February 26, 2005
Potential CSE Courses

Needs

We will begin with a series of joint CSE-Departmental Graduate program "tracks". For example, Computational Biology, Computational Chemistry, Computational Earth Systems, Computational Materials, Computational Mechanical Engineering, Computational Physics, and so forth. For the Graduate Group, we will need approximately 27 courses (9 per year). Some of these course may already be taught in existing departments, or perhaps existing courses could be modified to meet the CSE needs. We would anticipate using some courses already taught by departments as part of the core curriculum, and for some of the electives. In addition to basic ideas about programming, the CSE courses are intended to cover topics including the following. Note that most CSE courses will involve considerable computer programming.

• Applied Dynamical Systems
• Numerical Solution of Differential Equations with Multiple Timescales
• Computational Modeling and Simulation of Multiscale Systems and Processes
• Novel Computation in Physics, Chemistry, and Biology
• Physics of Computation
• Self-Organization and Statistical Mechanics
• Phase Transitions in Complex Systems - Nucleation and Scaling
• Computational Mechanics
• Advanced Computational Methods and Laboratory
• Networks in science and engineering
• Engineering complex systems
• Design and processing of complex materials
• Mathematics of systems

Sample Curriculum

Programming Basics

Fall Q: Intro to Procedural languages - Fortran and C
Winter Q: Intro to Object Oriented Programming - C++ and JAVA
Spring Q: Intro to Parallel and High Performance Computing, and Grid Computing

Basic Courses in Complex Systems - Team Taught

Fall Q: Introduction to Complex Systems
Winter Q: Dynamical Systems I (Low Dimensional Systems; Hamiltonian Systems & Maps)
Spring Q: Dynamical Systems II (High Dimensional Systems; Driven Threshold Systems)

Mathematical Methods and Analysis

Typical 1-year sequence as taught either by Physics or Applied Mathematics

Computational Methods in Data Analysis

Fall Q: Introduction to Scientific Visualization
Winter Q: Advanced and Immersive Visualization (Using Keck CAVES)
Spring Q: Data Mining and Statistical Analysis of Data (Includes ideas like Principal Component Analysis, Wavelets, time series analysis, etc.)

Simulations and Algorithms

Fall Q: Numerical Analysis (Linear systems, integration, ODEs, PDEs, etc)
Winter Q: Simulation Design, Analysis, and Optimization - I (Includes algorithms
for parallel environments, and grid computing; Monte Carlo
and lattice approaches)
Spring Q: Simulation Design, Analysis, and Optimization - II (Molecular Dynamics;
Density Functional Methods)

Advanced Complex Systems - I

Fall Q: Physics of Computation in Complex Systems (Models, Simulations, Data)
Winter Q: Patterns, Dynamics and Structure in Complex Systems
Spring Q: Forecasting and Prediction in Chaotic and Complex Systems

Advanced Complex Systems - II

Fall Q: Statistical Dynamics and Intrinsic Computation
Winter Q: Computational Mechanics - I
Spring Q: Computational Mechanics - II

Associated disciplinary courses in Physics, Mathematics, Biology, Engineering, Social
Systems, some taught either by affiliated departments, or in conjunction with those departments. For example:

1 Year Sequence in Engineering Complex Systems

Fall Q: Introduction to Engineering Systems
Winter Q: Chaos and Complexity in Complex Engineered Systems
Spring Q: Design and Control of Complex Systems in Engineering

1 Year Sequence in Complex Earth Systems

Fall Q: Introduction to Complex Earth Systems (Atmosphere, Ocean, Solid Earth,
Surficial Processes)
Winter Q: Simulating Earth Systems
Spring Q: Analysis and Visualization of Earth System Data (Includes simulation data and satellite data sets)