SmartUQ 11.1 Now Available!
SmartUQ provides advanced Machine Learning and Uncertainty Quantification tools that help our customers save time, decrease costs, improve quality, increase reliability, and reduce risk.
Our tools are optimized for engineering data sets from simulation, testing, manufacturing, and digital twins. SmartUQ goes beyond checking accuracy to quantify the disagreement and uncertainty in results and predictions. Becasue data is never perfect and models always have errors, knowing how far off they are from reality is critical when making engineering decisions.
High-accuracy, machine learning based emulators, including advanced flexible Gaussian process models, accelerate computationally heavy simulations across multi-dimensional, continuous, discrete, and varying geometry datasets.
Intelligent space-filling and uniform subsampling algorithms compress massive simulation and test datasets, preserving critical feature distributions and parameter sensitivities while slashing computational overhead.
Optimization algorithms couples with SmartUQ's automated learning and advanced Gausian process models to make optimization faster, instantly repeatable, and easily extensible to other analysis.
Novel space-filling DOEs and adaptive sampling methods maximize coverage and target critical boundaries with minimal simulation or test runs.
Advanced Bayesian calibration systematically tunes model parameters against physical test data, accounting for measurement noise and discrepancy.
Comprehensive global sensitivity analysis, aleatory/epistemic uncertainty propagation, and reliability-based optimization ensure robust, risk-informed decisions.
Physics-based simulations are used to train advanced Gaussian process-based surrogate models to accurately predict a wide variety of results. This means fewer computationally heavy simulations runs or expensive tests relative to both direct analysis or deep learning based methods.
Assess the agreement between simulations and physical data to establish model credibility. SmartUQ streamlines verification, validation, and uncertainty quantification (VVUQ) by aligning with standards and compliance directives from ASME V&V, FDA, DoD, and NASA etc.
Infer unobservable input parameters, material properties, and operational boundary conditions by mapping backward from observed physical test responses and sensor measurements to rapidly resolve simulation discrepancies.
SmartUQ's advanced sampling, Bayesian methods, and modeling algorithms are efficient enough to run entirely on local hardware. This means everything can be done on existing workstations and no data leaves your system. For bigger problems, the tools can be deployed accross Linux or Windows clusters.
“SmartUQ has the best prediction accuracy among all tools I have ever used.”
“SmartUQ's adaptive design can significantly reduce the number of required simulations [a 72% reduction] and lead to much higher model accuracy [96% reduction in reference prediction error]”
“Our Uncertainty Quantification discipline now uses SmartUQ as its central tool and with it we have helped save millions of dollars and thousands of hours of work.”
Modern Engineering Teams Need SmartUQ to cut development time, curb cost, and protect product integrity—before small issues become million-dollar problems.
SmartUQ offers Industry-Scale Uncertainty Quantification
SmartUQ: Predictive Analytics for Engineering and Physical Sciences
Manage your risk with SmartUQ
Gain the competitive edge with your analytics