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Predictive Analytics
What is Uncertainty Quantification?
Why Uncertainty Quantification?
SmartUQ 9.1 Now Available
UQ Goes Beyond Uncertainty Propagation
In addition to propagating input uncertainties, Uncertainty Quantification provides a more comprehensive framework, including several key analytics techniques:
- Building predictive models: trained to mimic complex engineering simulations.
- Sensitivity Analysis: ranks parameters by their ability to influence the results.
- Statistical Calibration: handles the disagreement and uncertainty between the simulation model and physical tests.
- Inverse Analysis: determines an underlying distribution for ill-conditioned and sparse model input.
Machine Learning for Changing Geometry Systems
Simulating domains with related but different geometries, meshes, or coordinate systems is a crucial piece of many workflows. Tasks such as geometry optimization, tolerance analysis, calibration to physical measurements, and uncertainty quantification on spatial responses all require handling varying geometries.
SmartUQ’s Changing Geometry Emulator extends the capabilities of SmartUQ’s spatial emulation to capture the effects of inputs on spatial responses and the effects of inputs on the resulting geometry or mesh. This opens up fast and accurate surrogate modeling for all kinds of simulations and spatial meassurments.
Predictive Analytics for Modern Engineering
Predictive Analytics encompass a set of advanced analytics techniques used to develop a predictive model for real time analysis and predicting future events. Using all types of data sets such as simulation modeling, manufacturing, and operational and sensor data, a trained SmartUQ predictive model can quickly perform complex analysis like predictive maintenance or risk analysis – giving your team a competitive advantage. Below are some applications of SmartUQ’s industry-scale predictive analytics:
- Virtual Sensors & Predictive Maintenance
- Reduce Simulation Demand
- Root Cause Analysis
- Model Calibration and Validation
- Digital Twins
UQ for Decision Making
No matter where the data comes from – simulation, physical testing, sensors, or a digital twin – there always is an element of uncertainty. Don’t depend on a deterministic point estimate that could be considerably off the mark and have costly consequences. By using Uncertainty Quantification to consider all the possible outcomes, you can optimize your decision making process resulting in reduced risk and greater confidence in your results.
See how SmartUQ optimizes decision-making from all data types, including:
- Additive Manufacturing
- Digital Twins
- Virtual Sensors
- Simulation
Acceleration with Analytics: Maximize Insights from Limited Data
Time and resources are always limited and sometimes important decisions must be made based on only a handful of data points. SmartUQ can help you get the most information out of the fewest points:
- Optimize sampling of new data while making use of existing data.
- Create balanced space filling samples while making use of existing data.
- Direct sampling to refine data sets only where necessary.
- Explore the entire design space through accurate emulation.
- Save time and resources by reducing design and testing iterations.
Take Analytics to the Next Level
We provide a wide spectrum of capabilities including:
- Design of experiments
- Emulation
- Statistical calibration
- Sensitivity analysis
- Propagation of uncertainty
- Statistical optimization
- Inverse analysis
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