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Johannes Sappl
Johannes Sappl
Ph.D. Student, Universität Innsbruck
在 uibk.ac.at 的电子邮件经过验证
标题
引用次数
引用次数
年份
A fully Lagrangian computational model for the integration of mixing and biochemical reactions in anaerobic digestion
M Rezavand, D Winkler, J Sappl, L Seiler, M Meister, W Rauch
Computers & Fluids 181, 224-235, 2019
392019
Deep learning of preconditioners for conjugate gradient solvers in urban water related problems
J Sappl, L Seiler, M Harders, W Rauch
arXiv preprint arXiv:1906.06925, 2019
202019
Machine learning for quantile regression of biogas production rates in anaerobic digesters
J Sappl, M Harders, W Rauch
Science of the Total Environment 872, 161923, 2023
152023
Accelerating surface tension calculation in SPH via particle classification and Monte Carlo integration
F Zorilla, M Ritter, J Sappl, W Rauch, M Harders
Computers 9 (2), 23, 2020
132020
Method for generating a compact representation of radar data, radar device and radar data processing circuit
P Meissner, M Haltmeier, FB Khalid, A Roger, J Sappl
US Patent 11,360,205, 2022
112022
On the effect of the inlet configuration for anaerobic digester mixing
S Dabiri, J Sappl, P Kumar, M Meister, W Rauch
Bioprocess and Biosystems Engineering 44 (12), 2455-2468, 2021
72021
Deep learning of preconditioners for conjugate gradient solvers in urban water related problems, 2019
J Sappl, L Seiler, M Harders, W Rauch
URL https://arxiv. org/abs, 1906
71906
Maschinelles Lernen in der Siedlungswasserwirtschaft
J Sappl, M Harders, W Rauch
Österreichische Wasser-und Abfallwirtschaft 7 (71), 359-368, 2019
22019
Low-rank approximation for FMCW automotive radar
J Sappl, P Meissner, M Haltmeier
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
12017
Vorhersage von Zeitserien der Biogasproduktion in anaeroben Faultürmen mit einem Temporal Fusion Transformer
J Sappl, M Harders, W Rauch
Österreichische Wasser-und Abfallwirtschaft 73 (7), 329-336, 2021
2021
Predicting biogas production rates in anaerobic digesters with a temporal fusion transformer
J Sappl, M Harders, W Rauch
2021
Machine Learning in Urban Water Management
J Sappl, M Harders, W Rauch
Österreichische Wasser-und Abfallwirtschaft 71, 359-368, 2019
2019
Deep Learning of Preconditioners for Acceleration of Secondary Settling Tank Simulations
J Sappl, S Dabiri, M Harders, W Rauch
Available at SSRN 4196783, 0
Deep Learning of Preconditioners for Conjugate Gradient Solvers in Urban Water Related Problems 2 Deep Learning of Preconditioners 3
J Sappl, L Seiler, M Harders, W Rauch
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