AI and Machine Learning for Simulation and Digital Twins
In order to capture the benefits of digital engineering, digital transformation initiatives have
become important to companies and organizations in all industries. Digital transformation calls
for greater use of simulation and the integration of simulation models in creating digital twins,
unlocking benefits such as improved decision-making, enhanced design confidence, and more
efficient engineering processes.
To achieve the aims of digital transformation modern AI and machine learning (ML) tools are
needed to handle challenges such as:
- Long simulation run times which can render statistical or iterative tasks like optimization,
sensitivity analysis, and uncertainty quantification infeasible.
- Difficulty extracting relevant information and quickly gaining insights from large,
correlated data sets, for example from sensor readings.
- Inaccuracy of models due to the presence of uncertainties arising from sources including
initial conditions, model parameters, model form, manufacturing, and operating
environment.
- Difficulty in obtaining fast, accurate digital twins.
To address these challenges and more, this course provides an introduction to important topics
including:
- How to optimally collect or select ML model training data using design of experiments
(DOEs) and data sampling.
- How to use ML to train surrogate models for accelerating simulation analyses.
- Use of surrogate models for sensitivity analysis, uncertainty propagation, and
optimization under uncertainty.
- Statistical calibration for achieving better agreement between models and physical data
and as a tool for creating digital twins.
Points will be illustrated with SmartUQ customer use cases and example problems
demonstrated in SmartUQ. Use cases and examples can be tailored to be relevant to the needs
and industry of the attendees.
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