Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning
arXiv:2606.17233v1 Announce Type: new Abstract: In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.g. finite element models of complex physical systems. To alleviate the high computational cost of direct model evaluations, surrogate models are widely used to construct efficient approximations of model responses. Naturally, the accuracy of surrogates strongly depends on the quality of the experimental design (ED). However, a single ED may not provide an adequate representation for all outputs simultan
The increasing complexity of engineering models and the rising computational costs necessitate more efficient and accurate uncertainty quantification methods, pushing research in this area.
Improved methods for uncertainty quantification in engineering applications can lead to more robust designs, reduced development costs, and safer systems, which is crucial for advanced technologies.
The development of more efficient surrogate models and active learning techniques will reduce the computational burden on simulating complex physical systems, accelerating R&D cycles.
- · Engineering simulation software providers
- · Aerospace and automotive industries
- · Advanced manufacturing
- · Traditional, purely brute-force simulation approaches
Reduced computational time and cost for complex engineering simulations, allowing for broader design space exploration.
Faster prototyping and validation cycles for new products and systems, increasing innovation speed.
Enhanced safety and reliability across critical infrastructure and high-stakes engineering applications due to better understanding of uncertainties.
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Read at arXiv cs.LG