- Engineering Workflow Automation
- Design Space Exploration
Building on existing engineering workflows, Optimus’ Design Space Exploration functions provide up-front insights into the unexplored design potential. They enable engineering teams to assess what is realistic in terms of achievable product performance and required development duration – altogether eliminating the need for an iterative trial-and-error engineering approach.
Dedicated Design of Experiments (DOE) methods define a minimum set of well-chosen engineering experiments to sample the design space most effectively, while the simulation robot automates the execution of this experiment set. By post-processing the DOE results, engineers easily discover the relative importance of design parameters and constraints.
The Optimus Adaptive DOE algorithm will even learn from the already available data points, and iteratively add extra data samples in design space regions that really matter. This will not just save a lot of time, but will deliver the best design space information that can be got for a given simulation budget.
Response Surface Modeling (RSM) condenses complex engineering processes into so-called surrogate models or meta-models, using the results of the engineering experiments defined by Design of Experiments. Surrogate models are very effective in evaluating new designs without requiring a full detailed analysis, or to provide valuable information on the relationship between design parameters and functional performance metrics.
Optimus' powerful Response Surface Modeling capabilities include Deep Neural Networks Modeling and Smart Modeling. Deep Neural Networks make it possible to reproduce the behavior of complex non-linear systems with almost arbitrary accuracy. Optimus' unique Best Model algorithm automatically builds meta-models that confidently model system response in all design regions of interest.
For Fuji Heavy Industries, the hills and valleys on an Optimus Response Surface Model provided valuable insights into non-linear winglet physics.
Design of Experiments and Response Surface Modeling techniques help engineers fully and rapidly grasp the unexplored design space potential early in the development process. As a result, they gain up-front insights into the product performance that is actually within reach. This in turn provides valuable knowledge on how to trade off multiple, often conflicting design objectives.