31 citations · 61 across the 3 of their papers we have counts for
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cs.LG2022★ 1 cited
Hybrid Machine Learning Modeling of Engineering Systems -- A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study
Timur Bikmukhametov, Johannes Jäschke
To operate process engineering systems in a safe and reliable manner, predictive models are often used in decision making. In many cases, these are mechanistic first principles mod…
cs.LG2021★ 29 cited
Multiple shooting for training neural differential equations on time series
Evren Mert Turan, Johannes Jäschke
Neural differential equations have recently emerged as a flexible data-driven/hybrid approach to model time-series data. This work experimentally demonstrates that if the data cont…