5 citations · 11 across the 7 of their papers we have counts for
7 papers
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Justus Arweiler, Indra Jungjohann, Aparna Muraleedharan +5
Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly…
Superstudent intelligence in thermodynamics
Rebecca Loubet, Pascal Zittlau, Marco Hoffmann +6
In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics e…
Using Large Language Models for Solving Thermodynamic Problems
Rebecca Loubet, Pascal Zittlau, Luisa Vollmer +5
Large Language Models (LLMs) have made significant progress in reasoning, demonstrating their capability to generate human-like responses. This study analyzes the problem-solving c…
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures
Dominik Gond, Jan-Tobias Sohns, Heike Leitte +2
Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing att…
KnowTD-An Actionable Knowledge Representation System for Thermodynamics
Luisa Vollmer, Sophie Fellenz, Fabian Jirasek +2
We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainabl…
Deep Anomaly Detection on Tennessee Eastman Process Data
Fabian Hartung, Billy Joe Franks, Tobias Michels +15
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tenn…