5 citations · 8 across the 8 of their papers we have counts for
8 papers
RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy
Mario Koddenbrock, Christoph Lange, Robin Legner +6
Machine learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive…
Automatic Raman Measurements in a High-Throughput Bioprocess Development Lab
Christoph Lange, Simon Seidel, Madeline Altmann +6
This study presents a collection of physical devices and software services that fully automate Raman spectra measurements for liquid samples within a robotic facility. This method…
Deep learning adaptive Model Predictive Control of Fed-Batch Cultivations
Niels Krausch, Martin Doff-Sotta, Mark Cannon +2
Bioprocesses are often characterised by nonlinear and uncertain dynamics, posing particular challenges for model predictive control (MPC) algorithms due to their computational dema…
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations
Christoph Lange, Isabel Thiele, Lara Santolin +4
In biotechnology Raman Spectroscopy is rapidly gaining popularity as a process analytical technology (PAT) that measures cell densities, substrate- and product concentrations. As i…
Handling nonlinearities and uncertainties of fed-batch cultivations with difference of convex functions tube MPC
Niels Krausch, Martin Doff-Sotta, Mark Canon +2
Bioprocesses are often characterized by nonlinear and uncertain dynamics. This poses particular challenges in the context of model predictive control (MPC). Several approaches have…
Latent State Space Extension for interpretable hybrid mechanistic models
Judit Aizpuru, Maxim Borisyak, Peter Neubauer +1
Mechanistic growth models play a major role in bioprocess engineering, design, and control. Their reasonable predictive power and their high level of interpretability make them an…
Deep Learning for Fast Inference of Mechanistic Models' Parameters
Maxim Borisyak, Stefan Born, Peter Neubauer +1
Inferring parameters of macro-kinetic growth models, typically represented by Ordinary Differential Equations (ODE), from the experimental data is a crucial step in bioprocess engi…
Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries
Maxim Borisyak, Stefan Born, Peter Neubauer +1
Cultivation experiments often produce sparse and irregular time series. Classical approaches based on mechanistic models, like Maximum Likelihood fitting or Monte-Carlo Markov chai…