3 citations · 8 across the 13 of their papers we have counts for
3 papers · 2 filters
Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
Mehmet Velioglu, Song Zhai, Sophia Rupprecht +3
In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) f…
Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
Eleni D. Koronaki, Luise F. Kaven, Johannes M. M. Faust +2
Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for…
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
Daniel Mayfrank, Na Young Ahn, Alexander Mitsos +1
Mechanistic dynamic process models may be too computationally expensive to be usable as part of a real-time capable predictive controller. We present a method for end-to-end learni…