Smart UQ Software

In general, an ability to predict and understand uncertainties enables for better decision making to mitigate the risks associated with those uncertainties.

In test planning, sampling-based sensitivity analyses can be run to identify the most critical system inputs to investigate. Predictive models can be used in real system applications, for example in predictive maintenance or training a model to act as a virtual sensor to provide readings on a fielded part or component that normally could only be collected under test settings due to cost or complexity. Rapid predictive models that can be recalibrated to changing physical conditions are also a key part of creating and maintaining an authoritative digital surrogate truth source for digital twin applications.

DOEs and data sampling allow users to select data that should be collected or to select a representative sample from a larger existing data set. In both cases the goal is intelligent collection or selection of data to best train an accurate predictive model. The data source can be physics-based simulation models, but also real world physical or experimental data. Once data has been collected, SmartUQ’s predictive modeling tools are used to build a statistical model of the underlying system or process that generated the training data. The rapid predictive capabilities of the model can then be leveraged to easily and efficiently perform analytics to gain insights into the system.

SmartUQ is a powerful predictive analytics and uncertainty quantification (UQ) software tool that incorporates real world variability and probabilistic behavior into engineering and systems analyses.

With a user-friendly GUI, SmartUQ software is designed for engineers to quickly and intuitively perform powerful analytics.

With advanced analytics at your fingertips, get accurate information to make important decisions such as how to maximize your product’s reliability and durability.
For example, using SmartUQ
statistical calibration feature, you can validate that your simulation agrees with reality, thus reducing model form uncertainty.

The library of predictive modeling options in SmartUQ covers a wide range of scenarios including predictive modeling for discrete and continuous inputs, spatial inputs, functional/transient responses, classification tasks, high dimensional problems, and very large sample sizes.

With a range of two-dimensional to four-dimensional plots with spatial and temporal components, SmartUQ enables you to explore your data and quickly recognize patterns that may emerge. In a few clicks, you can visualize large data sets and discover underlying relationships and potential outliers.

Reduce duration of simulation and testing. Catch problems early, reducing development time. Prevent unnecessary design iterations.

Increased utility of simulations. Fewer tests & prototypes. Reduce cost associated with unexpected failures.

SmartUQ has a flexible set of advanced predictive analytics and machine learning algorithms, including Gaussian process and kernel methods, neural network and deep learning.


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