1 citations · 1 across the 6 of their papers we have counts for
6 papers
Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation
Jakob Marcel Hoffmann, Yosuke Hasegawa, Alexander Stroh
Quantifying mitral regurgitation severity remains limited by the assumptions of clinical flow convergence methods, while high-fidelity simulation and volumetric velocimetry are too…
Interface reconstruction of adhering droplets for distortion correction using glare points and deep learning
Maximilian Dreisbach, Itzel Hinojos, Jochen Kriegseis +2
The flow within adhering droplets subjected to external shear flows has a significant influence on the stability and eventual detachment of the droplets from the surface. Most comm…
PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
Maximilian Dreisbach, Elham Kiyani, Jochen Kriegseis +2
Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conven…
Investigating the shortcomings of the Flow Convergence Method for quantification of Mitral Regurgitation in a pulsatile in-vitro environment and with Computational Fluid Dynamics
Robin Leister, Roger Karl, Lubov Stroh +12
The flow convergence method includes calculation of the proximal isovelocity surface area (PISA) and is widely used to classify mitral regurgitation (MR) with echocardiography. It…
Spatio-temporal reconstruction of drop impact dynamics by means of color-coded glare points and deep learning
Maximilian Dreisbach, Jochen Kriegseis, Alexander Stroh
The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas-liquid interface in two-phase flows on the…
Actively Learning Costly Reward Functions for Reinforcement Learning
André Eberhard, Houssam Metni, Georg Fahland +2
Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent…