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Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fields
arXiv:2507.17582v4 Announce Type: replace Abstract: Gaussian process regression techniques have been used in fluid mechanics for the reconstruction of flow fields from a reduction-of-dimension perspective. A main ingredient in this setting is the construction of adapted covariance functions, or kernels, to obtain such estimates. In this paper, we present a general method for constraining a prescribed Gaussian process on an arbitrary compact set.
Adaptation of the hybrid fictitious domain-immersed boundary method for Reynolds-averaged turbulence modeling
Announce Type: new Abstract: Engineering practice often calls for shape or topology optimization (TO) of fluid defining components, while the ever-increasing computing power allows the optimized cost functions to be based on computational fluid dynamics (CFD). However, a common bottleneck in CFD-based TO frameworks is the requirement for frequent remeshing. In order to alleviate this bottleneck, we propose an adaptation of an immersed boundary (IB) method variant, the hybrid fictitious...