1 citations · 3 across the 8 of their papers we have counts for
8 papers
Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles
Kai Dahms, Eilien Heinrich, Jochen Schmid +3
The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and polydispersity index (PDI),…
An adaptive discretization algorithm for locally optimal experimental design with constraints
Jochen Schmid, Philipp Seufert, Jan Schwientek +2
We develop a novel iterative algorithm for locally optimal experimental design under constraints, like budget or performance constraints. It is an adaptive discretization algorithm…
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Jennifer Werner, Justus Arweiler, Indra Jungjohann +4
Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rare…
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Mayank Nagda, Phil Ostheimer, Justus Arweiler +13
Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning mo…
An equation-based batch distillation simulation to evaluate the effect of multiplicities in thermodynamic activity coefficients
Jennifer Werner, Jochen Schmid, Lorenz T. Biegler +1
In this paper, we investigate the influence of multiplicities in activity coefficients on batch distillation processes. In order to do so, we develop a rigorous simulation of batch…
Cubature-based uncertainty estimation for nonlinear regression models
Martin Bubel, Jochen Schmid, Maximilian Carmesin +3
Calibrating model parameters to measured data by minimizing loss functions is an important step in obtaining realistic predictions from model-based approaches, e.g., for process op…
Adaptive discretization algorithms for locally optimal experimental design
Jochen Schmid, Philipp Seufert, Michael Bortz
We develop adaptive discretization algorithms for locally optimal experimental design of nonlinear prediction models. With these algorithms, we refine and improve a pertinent state…
Sequential optimal experimental design for vapor-liquid equilibrium modeling
Martin Bubel, Jochen Schmid, Volodymyr Kozachynskyi +2
We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in parti…