Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fields
Researchers have developed a novel method for reconstructing fluid flow fields using physics-informed Gaussian process regression. This technique incorporates boundary constraints directly into the regression process, allowing for more accurate estimations of flow dynamics. The approach has been demonstrated to effectively simulate fluid behavior around aerodynamic profiles without requiring boundary observations. AI