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Titel:

A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Nitzler, Jonas; Biehler, Jonas; Fehn, Niklas; Koutsourelakis, Phaedon-Stelios; Wall, Wolfgang A.
Abstract:
Two of the most significant challenges in uncertainty propagation pertain to the high computational cost for the simulation of complex physical models and the high dimension of the random inputs. In applications of practical interest both of these problems are encountered and standard methods for uncertainty quantification either fail or are not feasible. To overcome the current limitations, we propose a probabilistic multi-fidelity framework that can exploit lower-fidelity model versions of the...     »
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Computer Methods in Applied Mechanics and Engineering
Jahr:
2022
Band / Volume:
400
Nachgewiesen in:
Scopus
Volltext / DOI:
doi:https://doi.org/10.1016/j.cma.2022.115600
Status:
Verlagsversion / published
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