The numerical simulation of acoustic problems is, for itself, a quite difficult task since the underlying systems are usually highly complex with a broad frequency range and high sensitivity. Due to this complexity and the corresponding computational burden, tasks like optimization and uncertainty quantification (UQ) are seldom performed in acoustics. Especially when dealing with polymorphic uncertainties where combined techniques of UQ might be required, a direct use of the model is not viable. To allow such engineering tasks, the construction of a cheap surrogate or reduced model is common practice in order to allow a large number of model evaluations at low costs.
Paper
Full text
Gaussian process based surrogate modelling of acoustic systems
Semantic Scholar · Engineering · 2019
Abstract
The numerical simulation of acoustic problems is, for itself, a quite difficult task since the underlying systems are usually highly complex with a broad frequency range and high sensitivity. Due to this complexity and the corresponding computational burden, tasks like optimization and uncertainty quantification (UQ) are seldom performed in acoustics. Especially when dealing with polymorphic uncertainties where combined techniques of UQ might be required, a direct use of the model is not viable. To allow such engineering tasks, the construction of a cheap surrogate or reduced model is common practice in order to allow a large number of model evaluations at low costs.
References (14)
Scroll for more · 2 remaining