Kristian Gundersen

Statistics

Statistics:

Academic article

Show author(s) (2021). Semi-conditional variational auto-encoder for flow reconstruction and uncertainty quantification from limited observations. Physics of Fluids. 23 pages.

Show author(s) (2020). Binary time series classification with Bayesian convolutional neural networks when monitoring for marine gas discharges . Algorithms. 24 pages.

Report

Show author(s) (2019). Underpinning data for monitoring activities (including current statistics and carbon tracer quantifications). .

Academic lecture

Show author(s) (2019). Bayesian convolutional neural networks as a tool to detect discharges of pollutants to marine waters through time series classification.

Show author(s) (2018). The need for proper environmental statistics to design adequate monitoring for offshore geological storage of CO2 projects.
Show author(s) (2018). Machine Learning in CO2 leak detection.
Show author(s) (2018). Ensuring efficient and robust offshore storage – the role of marine system modelling.
Show author(s) (2018). Combining Models and Machine Learning Techniques to Design Leak Detection Monitoring .
Show author(s) (2018). Combining Environmental Statistics and Marine Process Modelling to Design Monitoring Programs for Offshore CO2 Storage.

Doctoral dissertation

Show author(s) (2021). Bayesian Variational Methods in Carbon Storage Monitoring.

Poster

Show author(s) (2020). Reconstruction of Currents Based on Sparse Observations with Deep Learning.

Show author(s) (2019). Ensuring efficient and robust carbon storage: marine modelling for environmental monitoring.

Show author(s) (2018). Model Reduction for Tracer Transport and Applications.
Show author(s) (2018). Mathematical methods for detection and localization of CO2 leaks.

Show author(s) (2017). Bayes’ theorem as the fundament to design monitoring programs.
Show author(s) (2017). Assessment of machine learning methods as a tool in detecting leakages.
Show author(s) (2017). Assessment of Topological Data Analysis and Machine Learning Technologies as Tools for Seep Detection.

Article in business/trade/industry journal

Show author(s) (2020). Semi Conditional Variational Auto-Encoder for Flow Reconstruction and Uncertainty Quantification from Limited Observations. arXiv.