Using Symbolic Computations to Develop Efficient Algorithms and System Models
In this webinar we show how engineers and scientists can use Symbolic Math Toolbox to develop efficient solutions to their technical problems. Whether you are developing algorithms or modeling engineering systems, there are often advantages to solving problems analytically, including:
• Efficiency – algorithms and models expressed analytically are often more efficient than equivalent numeric implementations
• Transparency – because they are in the form of math expressions, analytical solutions offer a clear view into how variables and interactions between variables affect the result, often helping you gain important insights (e.g. conditions that result in discontinuous regions, resonant frequencies, or a critically damped response)
Through product demonstrations we will show how the notebook interface provided in Symbolic Math Toolbox makes it easy to manage and document the steps taken to derive your analytical solutions, including any assumptions made. We will also show how analytical results computed in the notebook can be integrated with MATLAB and Simulink.
Presenter: Dan Doherty
• Efficiency – algorithms and models expressed analytically are often more efficient than equivalent numeric implementations
• Transparency – because they are in the form of math expressions, analytical solutions offer a clear view into how variables and interactions between variables affect the result, often helping you gain important insights (e.g. conditions that result in discontinuous regions, resonant frequencies, or a critically damped response)
Through product demonstrations we will show how the notebook interface provided in Symbolic Math Toolbox makes it easy to manage and document the steps taken to derive your analytical solutions, including any assumptions made. We will also show how analytical results computed in the notebook can be integrated with MATLAB and Simulink.
Presenter: Dan Doherty
Presenter Bio: Dan Doherty works as a product manager at The MathWorks, focusing on core math and data analysis products including Symbolic Math Toolbox. He has been with The MathWorks for over 6 years, in a variety of roles including product marketing specialist and product manager. Prior to joining The MathWorks, Dan received a B.S.E. and M.S.E. in Mechanical Engineering from the University of New Hampshire, where his research focused on prediction of cutting forces during CNC machining.
Recorded: 2 Mar 2010