51 citations · 117 across the 20 of their papers we have counts for
15 papers · 1 filter
Adjoint Complement to the Universal Momentum Law of the Wall
Niklas Kühl, Peter M. Müller, Thomas Rung
The paper is devoted to an adjoint complement to the universal Law of the Wall (LoW) for fluid dynamic momentum boundary layers. The latter typically follows from a strongly simpli…
Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic
Lucas Baier, Niklas Kühl, Jakob Schöffer +1
As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic. However, it r…
Human vs. supervised machine learning: Who learns patterns faster?
Niklas Kühl, Marc Goutier, Lucas Baier +2
The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provid…
Continuous Adjoint Complement to the Blasius Equation
Niklas Kühl, Peter M. Müller, Thomas Rung
The manuscript is concerned with a continuous adjoint complement to two-dimensional, incompressible, first-order boundary-layer equations for a flat plate boundary-layer. The text…
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
Lucas Baier, Vincent Kellner, Niklas Kühl +1
Machine learning models nowadays play a crucial role for many applications in business and industry. However, models only start adding value as soon as they are deployed into produ…
"Healthy surveillance": Designing a concept for privacy-preserving mask recognition AI in the age of pandemics
Niklas Kühl, Dominik Martin, Clemens Wolff +1
The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated t…
Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry
Jannis Walk, Niklas Kühl, Jonathan Schäfer
Sustainability is the key concept in the management of products that reached their end-of-life. We propose that end-of-life products have -- besides their value as recyclable asset…
DEAL: Deep Evidential Active Learning for Image Classification
Patrick Hemmer, Niklas Kühl, Jakob Schöffer
Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for supervised computer vision tasks, such as image classification. However, large labeled data sets…
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments
Lucia Schuler, Somaya Jamil, Niklas Kühl
Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. It promises the user services at large scale and low cost while eliminating…
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting
Robin Hirt, Niklas Kühl, Yusuf Peker +1
In a variety of business situations, the introduction or improvement of machine learning approaches is impaired as these cannot draw on existing analytical models. However, in many…
A network-based transfer learning approach to improve sales forecasting of new products
Tristan Karb, Niklas Kühl, Robin Hirt +1
Data-driven methods -- such as machine learning and time series forecasting -- are widely used for sales forecasting in the food retail domain. However, for newly introduced produc…
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Lucas Baier, Marcel Hofmann, Niklas Kühl +2
Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time, a phenomenon called concept drift. If not…
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
Robin Hirt, Akash Srivastava, Carlos Berg +1
In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the…
Machine Learning in Artificial Intelligence: Towards a Common Understanding
Niklas Kühl, Marc Goutier, Robin Hirt +1
The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interc…
Cahn-Hilliard Navier-Stokes Simulations for Marine Free-Surface Flows
Niklas Kühl, Michael Hinze, Thomas Rung
The paper is devoted to the simulation of maritime two-phase flows of air and water. Emphasis is put on an extension of the classical Volume-of-Fluid (VoF) method by a diffusive co…