ML and UQ for Structural Simulation

White Papers

How Statistical Methods Can Help You Get More Out of Your Simulation Tools
This paper focuses on solutions to three major challenges in the CAE design processes and how statistical methods are a powerful and viable approach to meet these challenges.
Digital Engineering Article: Take Uncertainty Out of Engineering Simulation
This article discusses how SmartUQ uncertainty quantification and analytics platform allows simulation to overcome ambiguities for better informed decision making.

Uncertainty Quantificiation and Machine Learning for Structural Simulation

Structural Finite Element Analysis (FEA) simulations play a crucial role in engineering by predicting how structures respond to various forces, identifying potential failures, and guiding design improvements. However, traditional structural FEA approaches often face challenges with computational costs and handling the uncertainties present in physical systems. SmartUQ addresses these limitations by employing sophisticated machine learning, uncertainty quantification, and advanced experimental design techniques.

Challenges in ML for Structural Simulation

High computational cost: high fidelity structural simulations may require hours or days per run, restricting thorough exploration of the design space and preventing use or sampling intensive analysis.
Complex parameter spaces: Many structural analysis involve large numbers of continuous and discrete parameters (e.g., geometries, loading conditions, noise profiles, material properties) that traditional ML methods cannot handle.
Resource-intensive calibration: Aligning simulation outputs with experimental or field data involves tuning both model parameters and model form, which can be manual and time-consuming.

SmartUQ Solutions for Structural Simulations

Design of Experiments: Generate optimized sampling patterns that maximize information across large numbers of continuous and discrete inputs.
Simulation Execution: Leverage SmartUQ’s automation interfaces and integrations to automatically run simulations and process results.
Surrogate Model Training: Train machine learning emulators on the collected simulation data, supporting high-dimensional, multifidelity, and spatial/temporal outputs.
Validation & Calibration: Validate surrogate accuracy against hold-out simulations or experimental measurements; perform statistical calibration to tune parameters and correct model form discrepancies.
Analytics & Decision Tools: Use the surrogate model to conduct uncertainty propagation, sensitivity studies, stochastic or reliability-based optimization, and dynamic emulation workflows that iteratively refine the model.

Key SmartUQ Features

Dynamic Emulation: Automatically run simulations, update the surrogate, and assess accuracy in a loop until a target error threshold or sample budget is met.
Dynamic Optimization & Contour Finding: Iterative sampling guided by surrogate variance to converge on optimal designs or level-set curves efficiently.
API & GUI Integrations: PySmartUQ for scripting within Python or MATLAB; GUI connectors for ANSYS Workbench, Adams, COMSOL; command-line and SmartSim I/O for other CFD platforms.

Transform Your Simulation Workflows

Start your SmartUQ trial today and raise your structural FEA workflows to new levels of efficiency, accuracy, and insight.

If you have an analytics challenge with your engineering simulation, email us at [email protected].

Machine Learning for Structural Simulation