22 citations · 139 across the 37 of their papers we have counts for
42 papers
Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data
Dan DeGenaro, Xin Li, Obed Amo +4
We introduce FLASH-MAX, a shallow, exact-by-construction neural network architecture for predicting homogeneous electromagnetic fields from sparse pointwise observations. Each hidd…
ECLASS-Augmented Semantic Product Search for Electronic Components
Nico Baumgart, Markus Lange-Hegermann, Jan Henze
Efficient semantic access to industrial product data is a key enabler for factory automation and emerging LLM-based agent workflows, where both human engineers and autonomous agent…
Physics-informed Gaussian Processes as Linear Model Predictive Controller with Constraint Satisfaction
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
Model Predictive Control evolved as the state of the art paradigm for safety critical control tasks. Control-as-Inference approaches thereof model the constrained optimization prob…
Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity
Obed Amo, Samit Ghosh, Markus Lange-Hegermann +2
We present a new benchmarking study comparing a boundary-constrained Ehrenpreis--Palamodov Gaussian Process (B-EPGP) surrogate with a classical finite element method combined with…
Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
Juhi Soni, Markus Lange-Hegermann, Stefan Windmann
We propose an unsupervised anomaly detection approach based on a physics-informed diffusion model for multivariate time series data. Over the past years, diffusion model has demons…
Approximately Equivariant Recurrent Generative Models for Quasi-Periodic Time Series with a Progressive Training Scheme
Ruwen Fulek, Markus Lange-Hegermann
We present a simple yet effective generative model for time series, based on a Recurrent Variational Autoencoder that we refer to as AEQ-RVAE-ST. Recurrent layers often struggle wi…
Linear ordinary differential equations constrained Gaussian Processes for solving optimal control problems
Andreas Besginow, Markus Lange-Hegermann, Jörn Tebbe
This paper presents an intrinsic approach for addressing control problems with systems governed by linear ordinary differential equations (ODEs). We use computer algebra to constra…
Gaussian Process Regression for Inverse Problems in Linear PDEs
Xin Li, Markus Lange-Hegermann, Bogdan Raiţă
This paper introduces a computationally efficient algorithm in system theory for solving inverse problems governed by linear partial differential equations (PDEs). We model solutio…
Physics-informed Gaussian Processes as Linear Model Predictive Controller
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
We introduce a novel algorithm for controlling linear time invariant systems in a tracking problem. The controller is based on a Gaussian Process (GP) whose realizations satisfy a…
Gaussian Process Priors for Boundary Value Problems of Linear Partial Differential Equations
Jianlei Huang, Marc Härkönen, Markus Lange-Hegermann +1
Working with systems of partial differential equations (PDEs) is a fundamental task in computational science. Well-posed systems are addressed by numerical solvers or neural operat…
Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
Jan Lippemeier, Stefanie Hittmeyer, Oliver Niehörster +1
Recent advancements in machine learning, particularly in deep learning and object detection, have significantly improved performance in various tasks, including image classificatio…
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes
Markus Lange-Hegermann, Christoph Zimmer
Experimental exploration of high-cost systems with safety constraints, common in engineering applications, is a challenging endeavor. Data-driven models offer a promising solution,…
PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
Henrik Folz, Joshua Henjes, Annika Heuer +7
In this paper, we explore the optimization of metal recycling with a focus on real-time differentiation between alloys of copper and aluminium. Spectral data, obtained through Prom…
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
Andreas Besginow, Jan David Hüwel, Thomas Pawellek +2
Model selection aims to find the best model in terms of accuracy, interpretability or simplicity, preferably all at once. In this work, we focus on evaluating model performance of…
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection
Nico Baumgart, Markus Lange-Hegermann, Mike Mücke
In industrial manufacturing, deploying deep learning models for visual inspection is mostly hindered by the high and often intractable cost of collecting and annotating large-scale…
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Jörn Tebbe, Christoph Zimmer, Ansgar Steland +2
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their cali…
Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders
Amandeep Singh, Michael Weber, Markus Lange-Hegermann
This paper addresses the challenges of detecting anomalies in cellular networks in an interpretable way and proposes a new approach using variational autoencoders (VAEs) that learn…
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
Marc Härkönen, Markus Lange-Hegermann, Bogdan Raiţă
Partial differential equations (PDEs) are important tools to model physical systems and including them into machine learning models is an important way of incorporating physical kn…
"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method
Ka Yung Cheng, Helmand Shayan, Kai Krycki +1
There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant…
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations
Andreas Besginow, Markus Lange-Hegermann
Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of G…
PGNAA Spectral Classification of Metal with Density Estimations
Helmand Shayan, Kai Krycki, Marco Doemeland +1
For environmental, sustainable economic and political reasons, recycling processes are becoming increasingly important, aiming at a much higher use of secondary raw materials. Curr…
On boundary conditions parametrized by analytic functions
Markus Lange-Hegermann, Daniel Robertz
Computer algebra can answer various questions about partial differential equations using symbolic algorithms. However, the inclusion of data into equations is rare in computer alge…
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design
Tanja Hernández Rodríguez, Anton Sekulic, Markus Lange-Hegermann +1
Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization…
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability
Tom Hammerbacher, Markus Lange-Hegermann, Gorden Platz
Digitalization leads to data transparency for production systems that we can benefit from with data-driven analysis methods like neural networks. For example, automated anomaly det…