4 papers
Machine learning correction of satellite precipitation is governed by mechanism purity, not algorithmic complexity: a proof-of-concept study in Hunan, China, with pre-registered cross-regional validation
Yi Xu
Satellite precipitation products such as IMERG exhibit biases that vary with terrain, season, and precipitation regime, leaving the applicability boundaries of machine learning cor…
Uncertain data assimilation for urban wind flow simulations with OpenLB-UQ
Mingliang Zhong, Dennis Teutscher, Adrian Kummerländer +3
Accurate prediction of urban wind flow is essential for urban planning, pedestrian safety, and environmental management. Yet, it remains challenging due to uncertain boundary condi…
OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations
Mingliang Zhong, Adrian Kummerländer, Shota Ito +3
Uncertainty quantification (UQ) is crucial in computational fluid dynamics to assess the reliability and robustness of simulations, given the uncertainties in input parameters. Ope…
Large-Scale Simulations of Turbulent Flows using Lattice Boltzmann Methods on Heterogeneous High Performance Computers
Adrian Kummerländer, Fedor Bukreev, Yuji Shimojima +2
Current GPU-accelerated supercomputers promise to enable large-scale simulations of turbulent flows. Lattice Boltzmann Methods (LBM) are particularly well-suited to fulfilling this…