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2023-04-26Type
- Other Conference Item
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Abstract
Nowadays, computational models have become an integral part of various fields of applied sciences and engineering. These models are used to forecast the behaviour of complex natural or man-made systems. Also known as simulators, they enable engineers and scientists to evaluate a system's performance in a virtual environment, and then help optimize its design or operation.
Simulators, such as high-fidelity finite element models, often comprise numerous parameters, and their execution is expensive, even when using the available computing power to the fullest. Additionally, the complexity of a system leads to greater uncertainty in its governing parameters, environmental and operating conditions. In this context, uncertainty quantification (UQ) methods have gained popularity in both academia and industry in recent times, as they can be used to address reliability, sensitivity, or optimal design problems. Monte Carlo simulation, a well-known brute-force method, uses random number generation to solve these questions. However, it may require thousands to millions of simulations to produce accurate predictions, making it impractical for high-fidelity simulators.
In contrast, surrogate models can solve these UQ problems by creating an accurate approximation of the simulator’s response, using a limited number of runs at selected values (the experimental design) and a learning algorithm. In this lecture, we will first introduce the general features of surrogate models and their relationship with machine learning. Next, we will discuss polynomial chaos expansions in detail, along with their sparse version for high-dimensional problems. We will also address recent extensions to structural dynamics. Show more
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publishedEvent
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03962 - Sudret, Bruno / Sudret, Bruno
Notes
Keynote 2. Conference lecture held on April 26, 2023.More
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