A DOE is a series of tests in which purposeful changes are made to the input variables
to investigate their effect upon the output responses and to get an understanding of the global behavior
of a design problem. By running a DOE, you can determine which factors are most influential on an output response.

An Optimization is a mathematical procedure used to determine the best design for a set of given constraints, by changing
the input variables in an automatic manner.

Optimization methods can be categorized, with respect to their search technique, as iterative or exploratory. Iterative
techniques can be either a local or global approximation.

A Stochastic approach is a method of probabilistic analysis where the input variables are defined by a probability
distribution, and consequently the corresponding output responses are not a single
deterministic value, but a distribution.

An Optimization is a mathematical procedure used to determine the best design for a set of given constraints, by changing
the input variables in an automatic manner.

Optimization methods can be categorized, with respect to their search technique, as iterative or exploratory. Iterative
techniques can be either a local or global approximation.

Optimization methods can be categorized, with respect to their search technique, as
iterative or exploratory. Iterative techniques can be either a local or global
approximation.

Local Approximation Method (Gradient Based)

Local approximation methods are effective when the sensitivities (derivatives) of the
system output responses with respect to input variables can be computed easily and
inexpensively.

Local approximation methods require design sensitivity analysis (DSA) and are most
suitable for linear static, dynamic and multi-body simulations.

Since finite difference calculations are expensive, DSA are preferred to be
calculated directly and therefore these methods are mostly integrated with FEA
Solvers. These methods are not feasible for non-linear solvers since they are
locally-oriented methods.

Global Approximation Method (Response Surface Based)

Global approximation methods are very efficient and hence they are preferred methods
when dealing with noisy non-linear output responses. Global optimization methods use
higher order polynomials to approximate the original structural optimization problem
over a wide range of input variables.

Exploratory Methods

Exploratory methods do not show the typical convergence of other optimization
algorithms. These algorithms efficiently search the design space, however they are
computationally expensive as they require large number of analysis. Rather than
exhibiting conventional convergence characteristics, a maximum number of evaluations
is defined.