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From the 1 of 5 linked papers with an AI index.

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5 papers

physics.ao-ph2026

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…

cs.MS2026

Automated Derivation of Lattice Boltzmann Methods for Systems of Conservation Laws

Adrian Kummerländer, Fedor Bukreev, Mathias J. Krause

The paper introduces an automated framework that derives lattice Boltzmann methods directly from systems of conservation laws, using a symbolic compiler to generate the necessary e…

physics.flu-dyn2026

Lattice Boltzmann Methods for Compressible (Magneto)hydrodynamics

Fedor Bukreev, Adrian Kummerländer, Mathias J. Krause

The simulation of magnetohydrodynamic (MHD) flows presents a highly complex, tightly coupled transport problem that poses severe numerical and computational demands. Towards this,…

physics.med-ph2025

An Integrated Open Source Software System for the Generation and Analysis of Subject-Specific Blood Flow Simulation Ensembles

Simon Leistikow, Thomas Miro, Adrian Kummerländer +6

Background and Objective: Hemodynamic analysis of blood flow through arteries and veins is critical for diagnosing cardiovascular diseases, such as aneurysms and stenoses, and for…

physics.comp-ph2025

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…