SmartUQ is a powerful Machine Learning (ML) software tool optimally designed for science and engineering applications. By providing powerful tools and highly accurate ML models with user-friendly GUIs and APIs, SmartUQ makes it easy to perform predictive modeling, optimized sampling, uncertainty quantification, and model calibration. From Fortune 500 manufacturers to startups and engineering consulting firms, SmartUQ's best in class predictive modeling accuracy helps our customers go beyond analysis to bring uncertainty into the decision-making process.
Why SmartUQ: SmartUQ's combination of unique sampling capabilities, powerful machine learning tools, and easy to use analytics help our customers solve previously unsolvable problems.
Industries Served: Automotive, Aerospace & Defense, Turbomachinery, Heavy Equipment, Medical Device, Semiconductors, Energy, Oil & Gas, HVAC
Tools and Application Areas:
-Acceleration of simulation efforts, Uncertainty Analysis, Testing and evaluation planning;
-Optimization under uncertainty, Robust design, Model calibartion and validation;
-Embedded models, virtual sensors, Root cause analysis, Manufacturing analytics;
-Digital twin analytics, Predictive Maintenance, Quality Control, Process Optimization.
Download here SmartUQ full brochure
Download here SmartUQ White paper
DOEs are typically used to collect new data from a system. In many cases, sufficient data has already been collected. Often in these scenarios, the data collected has been accumulated over long periods of time, and there is enough data that analysis is simply intractable. For example, health monitoring data from sensors on fielded components may capture live data continuously over the entire operating life of the component. SmartUQ’s data sampling tools can divide the data to mimic a space-filling DOE consisting of subsets of the full data set. Unlike DOEs which are developed before data collection, data sampling like subsampling and sliced sampling takes existing input-output data pairs and selects the points that will represent the design space well.
Designs of Experiments
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.
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.
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.
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.
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.
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.
In general, SmartUQ is very different in both purpose and capabilities from basic statistics packages.
SmartUQ is a predictive analytics engine focused on building an accurate prediction surface for all what-if input scenarios and quantifying various uncertainties in simulation and test. Once you have built with SmartUQ an accurate high-speed predictive model or emulator, it can be used to perform sensitivity analysis, uncertainty analysis, exploration of the entire design space, and optimization.
For uncertainty quantification, SmartUQ can significantly outperform Monte Carlo methods with the same sample size. SmartUQ also features statistical and Bayesian calibration which can significantly improve the accuracy of a simulation model using a small set of physical test points.
SmartUQ was invented to solve complex UQ problems. Designed from the ground up to enable practicing engineers to make use of advanced statistics, SmartUQ has a user-friendly interface and a powerful Python API for automation and integration with other tools.
SmartUQ has also focused development on advanced tools leading to a set of unique features including:
(1) The ability to handle UQ problems with multivariate, multi-fidelity, functional or spatial outputs, or with categorical and continuous inputs;
(2) Advanced Design of Experiments tools including several methods of adaptive design which choose new sampling points based on previously sampled design points;
(3) A variety of choices for statistical and Bayesian calibration;
(4) Technology for using scanned 3d surfaces as inputs for other UQ tools; and,
(5) The ability to handle problems with many more dimensions, larger sample sizes, and more complex structure than any previous tools. This is a crucial part of the success with customers in large engineering companies. None of these capabilities appear in any basic statistics packages.
Are you convinced, or do you wish to convince yourself? You can request a evaluation license at email@example.com. Do you have technical or other in-depth questions related to SmartUQ, you can contact firstname.lastname@example.org.
You can check here for the “Smart UQ_Full Brochure SC.pdf”
You can check here for the White paper “SmartUQ Engineering Analytics Light-Weighting Application SC.pdf”
Introduction to SmartUQ for Uncertainty Quantification and Propagation
Friday, March 17 Amsterdam Time: 2-3 PM
Registration Link: rb.gy/mi1xda
We are pleased to inform you that we have scheduled a special webinar that will be held in a friendly time: Friday 17th March from 2 o’clock in the afternoon till 3 o‘clock in the afternoon. Before all webinars were based on the Madison, Wisconsin time and that is 8 o’clock in the afternoon.
We are distributors of SmartUQ in the Benelux and are pleased to assist you in supporting SmartUQ. SmartUQ is a powerful ML and UQ software tool optimally designed for science and engineering applications. It was invented to solve modern ML and UQ problems such as those found in jet engine and gas turbine applications. As such, it is well suited for a plethora of ML and UQ applications across multiple industries including aerospace, automotive, heavy machinery, semiconductor, environmental, natural gas, and others. SmartUQ can be used for solving problems whether large or small, complex or simple.
This webinar is an introduction of Uncertainty Quantification (UQ). Uncertainty Quantification is a set of Machine Learning (ML) methods that puts error bands on results by incorporating real world variability and probabilistic behavior into engineering and systems analysis. UQ answers the question: what is likely to happen when the system is subjected to uncertain and variable inputs. Answering this question facilitates significant risk reduction, robust design, and greater confidence in engineering decisions.
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will provide an introduction to UQ in SmartUQ. The unique strengths and capabilities of SmartUQ will be highlighted, including tools for:
• Training fast and accurate machine learning models;
• Sensitivity Analysis;
• Uncertainty Propagation;
• Optimization under uncertainty;
• Statistical calibration;
Please send your questions, comments and feedback to: email@example.com or to firstname.lastname@example.org.
You are invited to our other free webinars scheduled as follows:
Machine Learning for Spatial and Temporal Response Prediction: SmartUQ’s Spatial/Temporal Emulator and Varying Geometry Module
March 23rd, 2023 time 19.00 hours Register Now!
Machine Learning for Prediction of High Dimensional Systems: SmartUQ’s Active Dimension Hybrid Emulator
April 5th, 2023 time 20.00 hours Register Now!
Machine Learning for Prediction of Systems with Discontinuous Response: SmartUQ’s Mixed Input Classification Emulator
April 20th, 2023 time 20.00 hours Register Now!
Machine Learning for Narrowing the Simulation-Test Gap: SmartUQ’s Data Matching and Bayesian Calibration
May 3rd, 2023 time 20.00 hours Register Now!
SmartUQ is a powerful Machine Learning (ML) and Uncertainty Quantiﬁcation (UQ) software tool optimally designed for science and engineering applications. It was invented to solve modern UQ and ML problems such as those found in jet engine and gas turbine applications. As such, it is well suited for a plethora of UQ and ML applications across multiple industries including aerospace, automotive, heavy machinery, semiconductor, environmental, natural gas, and others. SmartUQ can be used for solving problems whether large or small, complex or simple.
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ and comparing against OpenTURNS. Topics discussed will include SmartUQ’s design of experiments (DOEs), machine learning (aka surrogate) models, statistical calibration, inverse analysis tools, and data sampling of existing data sets. Benchmark examples comparing SmartUQ’s machine learning models to those of OpenTURNS will also be presented showing SmartUQ’s large advantages in terms of training speed and prediction accuracy.
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