3 papers
cs.LG2025
Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems
Arman Mohammadi, Mattias Krysander, Daniel Jung +1
Deep neural networks has been increasingly applied in fault diagnostics, where it uses historical data to capture systems behavior, bypassing the need for high-fidelity physical mo…
eess.SY2024
The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study
Daniel Jung, Erik Frisk, Mattias Krysander
This paper presents the LiU-ICE fault diagnosis benchmark. The purpose of the benchmark is to support fault diagnosis research by providing data and a model of an industrially rele…
cs.LG2023
Stability-Informed Initialization of Neural Ordinary Differential Equations
Theodor Westny, Arman Mohammadi, Daniel Jung +1
This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stabil…