294 citations
- SINTEFNO50 papers
- Norwegian University of Science and TechnologyNO30 papers
- University of OsloNO8 papers
- Oklahoma State UniversityUS6 papers
- Centre for Arctic Gas Hydrate, Environment and ClimateNO2 papers
- NTNU SamfunnsforskningNO2 papers
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- Simula Research LaboratoryNO2 papers
- Technische Universität BerlinDE2 papers
- UiT The Arctic University of NorwayNO2 papers
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- University of California, BerkeleyUS2 papers
5 papers · 1 filter
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…
Physics guided machine learning using simplified theories
Suraj Pawar, Omer San, Burak Aksoylu +2
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences…
Deep Reinforcement Learning for Long Term Hydropower Production Scheduling
Signe Riemer-Sorensen, Gjert H. Rosenlund
We explore the use of deep reinforcement learning to provide strategies for long term scheduling of hydropower production. We consider a use-case where the aim is to optimise the y…
Void Filling of Digital Elevation Models with Deep Generative Models
Konstantinos Gavriil, Georg Muntingh, Oliver J. D. Barrowclough
In recent years, advances in machine learning algorithms, cheap computational resources, and the availability of big data have spurred the deep learning revolution in various appli…