25 citations · 78 across the 12 of their papers we have counts for
12 papers
Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations
Berk Hadzhamolla, Alexander Johannes Stasik, Signe Riemer-Sørensen
Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation. Numerical approaches suc…
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Peter Collett, Alexander Johannes Stasik, Simone Casolo +1
Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertainty. While traditional Bayesi…
Analysis of Full-scale Riser Responses in Field Conditions Based on Gaussian Mixture Model
Jie Wu, Sølve Eidnes, Jingzhe Jin +5
Offshore slender marine structures experience complex and combined load conditions from waves, current and vessel motions that may result in both wave frequency and vortex shedding…
Testing Topological Data Analysis for Condition Monitoring of Wind Turbines
Simone Casolo, Alexander Stasik, Zhenyou Zhang +1
We present an investigation of how topological data analysis (TDA) can be applied to condition-based monitoring (CBM) of wind turbines for energy generation. TDA is a branch of dat…
Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling
Pål V. Johnsen, Eivind Bøhn, Sølve Eidnes +2
Time-series modeling in process industries faces the challenge of dealing with complex, multi-faceted, and evolving data characteristics. Conventional single model approaches often…
Pseudo-Hamiltonian system identification
Sigurd Holmsen, Sølve Eidnes, Signe Riemer-Sørensen
Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as fir…
Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces
Sølve Eidnes, Alexander J. Stasik, Camilla Sterud +2
Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conservin…
Mutual information estimation for graph convolutional neural networks
Marius C. Landverk, Signe Riemer-Sørensen
Measuring model performance is a key issue for deep learning practitioners. However, we often lack the ability to explain why a specific architecture attains superior predictive ac…
A Levenberg-Marquardt algorithm for sparse identification of dynamical systems
Mark Haring, Esten Ingar Grøtli, Signe Riemer-Sørensen +2
Low complexity of a system model is essential for its use in real-time applications. However, sparse identification methods commonly have stringent requirements that exclude them f…
Improved VIV response prediction using adaptive parameters and data clustering
Jie Wu, Decao Yin, Halvor Lie +3
Slender marine structures such as deep-water riser systems are continuously exposed to currents leading to vortex-induced vibrations (VIV) of the structure. This may result in ampl…
Data-driven prediction of vortex-induced vibration response of marine risers subjected to three-dimensional current
Signe Riemer-Sørensen, Jie Wu, Halvor Lie +2
Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation…
Origin of Metals around Galaxies I: Catalogs of Metal-line Absorption Doublets from High-Resolution Quasar Spectra
Lluís Mas-Ribas, Signe Riemer-Sørensen, Joseph F. Hennawi +5
We present the first paper of the series Origin of Metals around Galaxies (OMG) aimed to study the origin of the metals observed in the circumgalactic and intergalactic media. In t…