The DOE component enables engineers to quickly assess the impact of various design variables based on a set of objectives and identify significant interactions. The design data produced by DOE runs can also be used with approximation models for use with optimization methods.
Isight provides stochastic methods that account for variation in product designs and their operating environment. The Monte Carlo Simulation (MCS) component offers an accurate method to address uncertainty and randomness in the design process. It allows users to sample the design space, assess the impact of known uncertainties in input variables on the system responses, and characterize the statistical nature (mean, variance, range, distribution, etc.) of the responses of interest.
Isight provides a comprehensive selection of parallelized optimization techniques that can be applied to a variety of problems. It also includes techniques that can handle multi-objective optimization problems.
Isight offers powerful real-time tools to interpolate results of computationally intensive realistic simulations. Approximation models are automatically cross-validated to ensure accurate predictions. The Visual Design Driver allows users to see their approximation models from many different views and “surf” the design space graphically and interactively.
This process enables simulation models to be calibrated by minimizing any variety of different error measures using optimization techniques. Target data can be imported as experimental or simulation results.
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