On the use of stochastic spectral methods in deep excavation inverse problems
• We use stochastic surrogate models and Bayesian methods to solve inverse problems. • We develop an analytical formulation to determine the parameter influence in response. • We propose an algorithm to bound the truncation error in surrogate models. • We propose an iterative strategy that converge...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cañavate-Grimal, Antonio [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Umfang: |
20 |
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| Übergeordnetes Werk: |
Enthalten in: Conference Committee and Organizers - 2016, an international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:159 ; year:2015 ; day:15 ; month:10 ; pages:41-60 ; extent:20 |
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| DOI / URN: |
10.1016/j.compstruc.2015.06.009 |
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| 520 | |a • We use stochastic surrogate models and Bayesian methods to solve inverse problems. • We develop an analytical formulation to determine the parameter influence in response. • We propose an algorithm to bound the truncation error in surrogate models. • We propose an iterative strategy that converge to the inverse problem solution. • We avoid Monte Carlo Markov Chains to solve the inverse problem. | ||
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• We use stochastic surrogate models and Bayesian methods to solve inverse problems. • We develop an analytical formulation to determine the parameter influence in response. • We propose an algorithm to bound the truncation error in surrogate models. • We propose an iterative strategy that converge to the inverse problem solution. • We avoid Monte Carlo Markov Chains to solve the inverse problem. |
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• We use stochastic surrogate models and Bayesian methods to solve inverse problems. • We develop an analytical formulation to determine the parameter influence in response. • We propose an algorithm to bound the truncation error in surrogate models. • We propose an iterative strategy that converge to the inverse problem solution. • We avoid Monte Carlo Markov Chains to solve the inverse problem. |
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• We use stochastic surrogate models and Bayesian methods to solve inverse problems. • We develop an analytical formulation to determine the parameter influence in response. • We propose an algorithm to bound the truncation error in surrogate models. • We propose an iterative strategy that converge to the inverse problem solution. • We avoid Monte Carlo Markov Chains to solve the inverse problem. |
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