Our flagship product is SmartUQ: a powerful uncertainty quantification and analytics software platform. Based on solutions from our breakthrough research, SmartUQ is designed to reduce the time, expense, and uncertainty associated with simulations, testing, and analyzing complex systems. When analytics and uncertainty quantification are fast and easy, they can be applied to new areas of your design cycles, allowing you to make high-impact decisions with greater confidence.
SmartUQ provides a number of breakthrough data sampling techniques and a comprehensive library of advanced DOE generators for both simulation and physical experiments. Invented by thinking outside the box, our technologies ensure accuracy and minimize the number of data points required to generate uncertainty quantification and analytics results. Several of our more popular tools include subsampling for Big Data applications and Adaptive Design, which maximizes sampling efficiency by using already gathered data to select additional data points.
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Game-changing emulation technology allows SmartUQ to fit accurate emulators in record-setting time. These extremely fast analytical models can predict the behavior of complex black-box computational and physical systems. Using emulators enables extremely fast uncertainty propagation, sensitivity analysis, design space exploration, statistical optimization, statistical calibration, and inverse analysis. No more expensive Monte Carlo sampling and no more waiting hours for analytics calculations.
SmartUQ’s technology can handle categorical and continuous inputs, systems with multiple and functional outputs, high dimensional systems, and big data, opening new doors for accelerating uncertainty quantification and analytics.
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Rapidly determine the sensitivity of outputs with respect to inputs across the entire design space. This is useful when determining sensitivity of part geometries, instrumentation accuracy, and regulatory compliance with respect to manufacturing tolerances, environmental conditions, and wear levels. Sensitivity analysis shows which factors have a relatively low or high impact, allowing engineers to focus design effort and resources where they are needed most.
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Propagation of uncertainty lets users predict the probability distributions of system outputs resulting from distributions of uncertain or variable system inputs. Almost all systems have some input uncertainty usually from inputs like physical measurements, manufactured dimensions, material properties, environmental condition, and applied forces. Propagation of uncertainty helps engineers determine whether the system outputs will meet requirements, what the extreme probabilities really are, and which inputs have the most effect on the output distributions. All this means better initial designs, faster development, and simplified trouble shooting.
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SmartUQ can be used to conduct statistical optimization. This novel approach combines adaptive sampling techniques and analytical models providing improved performance on complex problems relative to search based methods. Statistical optimization also allows very rapid search area reduction with multiple objectives and very large numbers of input parameters. Even better, the required system evaluations may be determined using adaptive design, recycled from earlier data sets, or run in parallel batches for large clock-time savings and shortened testing cycles.
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Simulation accuracy continues to improve but it is still necessary to ground simulations with test data to ensure that they accurately represent the real world. Our statistical calibration tool quickly and automatically determines model calibration parameters given limited simulation and test data. It also provides model discrepancy measurements to help identify opportunities for improvements and to provide metrics for model validation. By increasing model accuracy and accelerating model validation, statistical calibration can decrease the time and number of tests required to understand complex systems, shortening the design cycle.
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Inverse analysis is the process of determining the probability distribution of an input resulting in a set of outputs from a system. This process is broadly useful for determining all kinds of hard-to-measure system properties. A good example is the determination of soil properties given a model of ground-vehicle interaction and vehicle telemetry data. Inverse analysis only requires a model of the system and a test data set for which the input distribution is to be determined. This allows you to take advantage of existing or easily obtainable data rather than resorting to expensive testing or invasive/destructive measurement techniques.
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