Response Surface Modeling (RSM) condenses complex engineering simulations into so-called surrogate models, meta-models, or approximation models - using the results of virtual or physical prototyping experiments defined by Design of Experiments. It involves a collection of mathematical and statistical methods that help model and analyze problems in which a response of interest is impacted by several parameters.

Response surface models are extremely efficient in providing valuable information about the relationship between design parameters and response metrics. They make it possible to evaluate new designs in seconds without requiring a full detailed engineering analysis, or to perform ultrafast design optimization. In particular when the underlying physics simulations are computationally expensive, the RSMs are a real time saver.

With the new Optimus 2019.01 release, several new modeling methods have been added that further extend Optimus' powerful Response Surface Modeling capabilities. During this webinar, we'll present Optimus' new Deep Neural Networks Model and its Smart Models family.