cs.CE · 2026-08-19 · No. 89
Computational Engineering, Finance, and Science, 2026-08-19.
2 new papers in cs.CE. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
2 entries-
01
Adaptive surrogate modeling for high-dimensional spatio-temporal output
Berkcan Kapusuzoglu, Shunsaku Matsumoto, Yoshitomo Miyagi, Daigo Watanabe, Sankaran Mahadevan
cs.CE · cs.AI · cs.LG · stat.ME · stat.ML
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...
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02
Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data
Berkcan Kapusuzoglu, Sankaran Mahadevan
cs.CE · cs.LG · stat.ME · stat.ML
When computational models (either physics-based or data-driven) are used for the sensitivity analysis of engineering systems, the sensitivity estimate is affected by the accuracy and uncertainty of the model. This paper considers global sensitivity analysis (GSA) for situations where both a physics-based model and experimental observations are available, and investigates physics-informed machine learning strategies to effectively combine the...
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