ESM 5984 - Project Contract
Experimental Optimization Through Visualization
Send Suggestions or Comments to firstname.lastname@example.org
- Optimization can usually be accomplished with one of many gradient techniques
that ensure quick and accurate convergence to a desired extremum of a particular objective
function. These techniques are most reliable, however, when the function is "well-behaved".
Gradient methods tend to fail when the objective function contains many local extrema. In this
case, random search methods (such as genetic algorithms) can be used to locate a global
extreme at the cost of requiring much longer computation times. An overwhelming advantage
of these methods are their simplicity. For even more intricate objective function behaviour,
random search methods may not be able to determine the extrema reliably. It is suggested that
more insight and reliability can be obtained through visualization of the objective function. Using
this method should require a complete parametric evaluation of the function, but retains an
encouraging element of simplicity.
- Even though a visualization method can be applied to an enormous range of problems,
this study will
focus on a specific experimental optimization problem.
When new materials are invented the
thermophysical properties of the material are usually unknown. For example, our research group
is frequently interested in estimating the thermophysical properies of new composites that exhibit
anisotropic behavior. To estimate the properties we must design an experiment that will give us
the best results. A typical experimental optimization procedure is extremely complicated and
usually is performed using a parametric approach, and only one criterion (for determining the "best"
experiment) is used.
I suggest that a visual approach can locate the "best" experiment in terms of many criteria.
- To identify the criteria to be used to determine the "best" experiment.
- To visualize several optimization criteria as a function of the parameters to be optimized.
- To locate experimental parameters that produce the "best" experiment.
- To verify the ability of the experiment to accurately compute the parameters better than previous
- To record my findings on this particular problem as well as extrapolations of the method to other
situations in a small multi-media presentation.
- The parametric study of the optimization criteria will be performed using a Fortran program (on
any platform). The visualization will be accomplished using SpyGlass Dicer and/or AVS. (Even
though I would prefer an NCSA viz tool, I would like to learn more about MAC's because I have
never used one.) The multi-media presentation will be performed using Director (I think).
Last updated: March 2, 1996