Blar i NTNU Open på tittel
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Physicochemical mechanisms for gas adsorption on clay mineral interfaces and surfaces
(Doctoral theses at NTNU;2020:316, Doctoral thesis, 2020)Increasing anthropogenic carbon emissions have a detrimental impact on our planet's ecosystems. If we want to keep our planet habitable for future generations, serious action is needed. We need to both reduce and mitigate ... -
Physics guided machine learning using simplified theories
(Peer reviewed; Journal article, 2021)Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular ... -
Physics Guided Machine Learning: Injecting neural networks with simplified theories
(Master thesis, 2021)Eksponentiell vekst i datakraft og tilgjengelighet av store datasett har popularisert og forbedret maskinlæring betydelig de siste årene. Nevrale nettverk er sterke verktøy som kan oppdage mønstre i komplekse datasett og ... -
Physics guided neural networks for modelling of non-linear dynamics
(Peer reviewed; Journal article, 2022)The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human ... -
Physics informed neural networks in radial load flow calculations
(Master thesis, 2023)Denne avhandlingen undersøker hvordan fysikkinformerte nevrale nettverk kan brukes til å løse lastflytberegninger i radielle distribusjonsnett. Mer spesifikt brukes tensorflow til å etablere nevrale nettverk i Python, som ... -
Physics of Strain Burst in Hard Rock – Fracture characteristics and influences of the stiffness of the host rock mass
(Doctoral theses at NTNU;2022:242, Doctoral thesis, 2022)Rock burst is a phenomenon in which rock disintegrates, accompanied by the ejection of debris. Strain burst is a type of rock burst. It is directly associated with stress concentration after rock excavation. Overstressed ... -
A physics-, SCADA-based remaining useful life calculation approach for wind turbine drivetrains
(Peer reviewed; Journal article, 2022)This paper describes the development of a physics-, SCADA-based model able to predict the expected lifetime for wind turbine drivetrains. A real-time coupled torsional gearbox-generator model is developed using the bond ... -
Physics-based Analytical Engineering Models of Graphene Micro- and Nanostrip Lines
(Journal article; Peer reviewed, 2019)In this paper, new approximate analytical models of graphene-based micro-and nanostrip transmission lines are given. These models are based on the representation of the mentioned lines by a parallel-plate waveguide embedded ... -
Physics-based and data-driven reduced order models: applications to coronary artery disease diagnostics
(Doctoral theses at NTNU;2020:362, Doctoral thesis, 2020)In this thesis we have developed reduced-order models for the prediction of pressure and flow in the arterial system and for the diagnosis of coronary artery disease. By reduced-order model we refer to a reduction of ... -
Physics-based characterization of soft marine sediments using vector sensors
(Peer reviewed; Journal article, 2021)In a 2007 experiment conducted in the northern North Sea, observations of a low-frequency seismo-acoustic wave field with a linear horizontal array of vector sensors located on the seafloor revealed a strong, narrow peak ... -
A Physics-Data Co-Operative Ship Dynamic Model for a Docking Operation
(Journal article; Peer reviewed, 2022)The development of onboard sensors is bringing us to the next level of ship digitalization. Its ultimate goal is to ensure safe & efficient marine operation by ship intelligence. In particular, during a docking operation, ... -
Physics-data Cooperative Modeling for Ship Motion Prediction
(Doctoral theses at NTNU;2022:206, Doctoral thesis, 2022)Increasing development on autonomous vehicles and concern on ship navigation safety put forward a higher requirement for ship motion forecasting technology. The predictions of ship motion in the near future can give the ... -
Physics-data cooperative ship motion prediction with onboard wave radar for safe operations
(Chapter, 2023)The advancement of sensing technologies brings digitalization into the field of offshore operations. Especially, practitioners have paid attention to ensuring operational safety by predicting ship motion with motion sensors ... -
Physics-informed and learning-based approaches to biomedical hyperspectral data analysis
(Doctoral theses at NTNU;2021:157, Doctoral thesis, 2021)The high spectral and spatial resolution of hyperspectral imaging makes it a promising imaging technique for a wide range of biomedical applications. A recurring challenge is the handling and processing of the large amounts ... -
Physics-Informed Bayesian Calibration Accounting for Model Discrepancy in a Linearized Homogeneous Ordinary Differential Equation
(Master thesis, 2023)Denne masteroppgåva utforskar eit fysikkinformert, fullstendig bayesiansk rammeverk for parameterestimering og usikkerheitsanalyse i lineariserte homogene ordinære differensiallikningar (ODE-ar). Vi undersøkjer korleis eit ... -
Physics-Informed Neural Networks for Dynamic Modeling of Pipeline-Riser Systems
(Master thesis, 2022)Vi introduserer fysikkinformerte nevrale nettverk på en Pipeline-Riser-modell. Fysikkinformerte nevrale nettverk er nevrale nettverk som er trent til å løse overvåket læringsoppgave mens de følger et fysisk prinsipp uttrykt ... -
Physics-Informed Neural Networks for Modeling and Control of Gas-Lifted Oil Wells
(Master thesis, 2022)Physics-Informed Neural Networks (PINNs) er en metode for å trene et nevral nettverk til å gjenskape oppførselen til et dynamisk system uten å ha tilgang til simulerte eller målte data, kun ved å bruke den kjente underliggende ... -
Physics-Informed Neural Networks for Modeling of Electric Submersible Pumps in Oil Wells
(Master thesis, 2023)Physics-Informed Neural Networks (PINN) er nevrale nettverk som inkluderer kjente fysiske sammenhenger inn i treningsprosessen sin, slik at de kan modellere fysiske systemer effektivt, selv med begrenset tilgang til ... -
Physics-Informed Statistical Machine Learning and Methods for Digital Twins
(Doctoral theses at NTNU;2023:177, Doctoral thesis, 2023)