Projects per year
Abstract
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications. Multilevel Delayed Acceptance (MLDA) with an Adaptive Error Model (AEM) is a novel approach, which alleviates this problem by exploiting a hierarchy of models, with increasing complexity and cost, and correcting the inexpensive models on-the-fly. The method has been integrated within the open-source probabilistic programming package PyMC3 and is available in the latest development version. In this paper, the algorithm is presented along with an illustrative example.
Original language | English |
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Number of pages | 8 |
Publication status | Published - 10 Dec 2020 |
Event | Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing Systems 2020 - Duration: 10 Dec 2020 → 10 Dec 2020 https://ml4eng.github.io/ |
Workshop
Workshop | Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing Systems 2020 |
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Period | 10/12/20 → 10/12/20 |
Internet address |
Bibliographical note
8 pages, 4 figures, accepted for Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing Systems 2020Keywords
- stat.CO
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Re-shaping the Test Pyramid
Butler, R. (PI), Anaya-Izquierdo, K. (CoI), Rhead, A. (CoI) & Scheichl, R. (CoI)
Engineering and Physical Sciences Research Council
1/08/19 → 31/07/25
Project: Research council
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Multiscale Modelling of Aerospace Composites
Butler, R. (PI) & Scheichl, R. (CoI)
Engineering and Physical Sciences Research Council
6/01/14 → 5/02/18
Project: Research council