(Editor’s note: This announcement is excerpted and mildly reformatted from an Oct. 5 email from Tom McHugh, Tom.McHugh@mathworks.com.)
Please join MathWorks for complimentary MATLAB seminars Wednesday, Oct. 24, in 401 Hale Library. Register now at mathworks.com/seminars/KSU2012 for these sessions on “Mathematical Modeling & Parallel Computing with MATLAB at Kansas State University”. Presenter: Saket Kharsikar, application engineer.
Mathematical Modeling with MATLAB
9:30 a.m.–noon Wednesday, Oct. 24, Hale 401
Mathematical models are critical to understanding and accurately simulating the behavior of complex systems. They enable important tasks such as forecasting system behavior for various “what if” scenarios, characterizing system response, and designing control systems.
This session will show how you can use MATLAB products for mathematical modeling tasks, including:
- Developing models using data fitting and first-principle modeling techniques
- Optimizing the accuracy of mathematical models
- Simulating models and post-processing the results
- Documenting and sharing models
You will also learn about different approaches you can use to develop models, including developing models programmatically using the MATLAB language, deriving closed-form analytical equations using symbolic computation, and leveraging prebuilt graphical tools for specific modeling tasks such as curve and surface fitting.
Parallel Computing with MATLAB
1:30–3 p.m. Wednesday, Oct. 24, Hale 401
In this session you will learn how to solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. We will introduce you to high-level programming constructs that allow you to parallelize MATLAB applications and run them on multiple processors. We will show you how to overcome the memory limits of your desktop computer by distributing your data on a large-scale computing resource, such as a cluster. We will also demonstrate how to take advantage of GPUs to speed up computations without low-level programming.
- Toolboxes with built-in support for parallel computing
- Creating parallel applications to speed up independent tasks
- Programming with distributed arrays to work with large data sets
- Scaling up to computer clusters, grid environments, or clouds
- Employing GPUs to speed up your computations