SmartUQ develops AI-driven software for engineering analytics and uncertainty quantification, enabling accurate predictive modeling and optimized sampling for complex systems. The platform addresses challenges in simulation, digital twins, and testing by significantly reducing simulation demands and improving model accuracy, resulting in substantial cost and time savings for users.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering simulations, digital twins, and physical testing of complex systems often require extensive computational resources and time, while still struggling with uncertainties that limit the accuracy of predictive models. This can lead to suboptimal designs, increased costs, and delays in product development.
Solution
SmartUQ provides AI-driven software for engineering analytics and uncertainty quantification, enabling more accurate predictive modeling and optimized sampling for complex systems. The platform leverages machine learning algorithms to build predictive models from various data sources, including simulation, manufacturing, and sensor data. By reducing simulation demands and improving model accuracy, SmartUQ helps users gain deeper insights, optimize designs, and make better decisions with limited data. The software offers user-friendly interfaces and APIs, facilitating integration into existing engineering workflows.
Target Audience
The primary target audience includes engineers, scientists, and analysts in industries such as aerospace, automotive, manufacturing, and energy, who are involved in simulation, digital twins, and physical testing of complex systems.
Features
- AI-powered predictive modeling for real-time analysis and predicting future events
- Uncertainty Quantification (UQ) techniques to manage risk and optimize decision-making
- Varying Geometry Emulator to build ML models of spatial systems without mesh or coordinate restrictions
- Statistical Calibration tools to improve the fidelity of simulation models with physical data
- Sensitivity analysis to rank parameters by their ability to influence results
- Design of experiments (DOE) capabilities to optimize sampling of new data
- Inverse analysis to determine an underlying distribution for ill-conditioned and sparse model input