Please join MathWorks for two different MATLAB seminars on Thursday, March 7, 2013 at NYU’s Kimmel Center, Room 802 (Shorin Studio).
**These technical seminars are FREE for all NYU faculty, staff, researcher and students.**
Tips and Tricks for using MATLAB at New York University
Presenter: Gerardo Hernandez, MathWorks Technical Application Engineer
Session 1: Mathematical Modeling with MATLAB
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.
Session 2: Parallel Computing with MATLAB
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 without CUDA or MPI programming and run them on multiple processors. We will also show you how to overcome the memory limits of your desktop computer and solve problems that require manipulating very large matrices by distributing your data.
• 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
• Tips on developing parallel algorithms