Adaptive surrogate modeling for high-dimensional spatio-temporal output
This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics systems is computationally expensive and consists of a large number of inputs and outputs. Surrogate models are often constructed to replace the physics-based model to achieve computational efficiency in analyses such as uncertainty quantification and optimization that require many function calls. In order to address the challenge introduced by the high dimensionality of spatio-temporal output, a dimension reduction method is first employed to map the high-dimensional output to a low-dimensional latent space. This is follo...
| Authors | Berkcan Kapusuzoglu, Shunsaku Matsumoto, Yoshitomo Miyagi et al. |
|---|---|
| Primary category | cs.CE |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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