Blar i NTNU Open på forfatter "Blakseth, Sindre Stenen"
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Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2022)Upcoming technologies like digital twins, autonomous, and artificial intelligent systems involving safety–critical applications require accurate, interpretable, computationally efficient, and generalizable models. ... -
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2022)In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA)—a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and ... -
Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions
Sørbø, Sondre; Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2024)With the recent wave of digitalization, specifically in the context of safety–critical applications, there has been a growing need for computationally efficient, accurate, generalizable, and trustworthy models. Physics-based ... -
Hybrid Dynamic Surrogate Modelling for a Once-Through Steam Generator
Blakseth, Sindre Stenen; Andersson, Leif Erik; Mocholí Montañés, Rubén; Mazzetti, Marit Jagtoyen (Peer reviewed; Journal article, 2023)Four surrogate modelling techniques are compared in the context of modelling once-through steam generators (OTSGs) for offshore combined cycle gas turbines (GTCCs): Linear and polynomial regression, Gaussian process ... -
Introducing CoSTA: A Deep Neural Network Enabled Approach to Improving Physics-Based Numerical Simulations
Blakseth, Sindre Stenen (Master thesis, 2021)Hybrid analyse og modellering (HAM) er et fremvoksende modelleringsparadigme hvor fysikkbasert modellering (FBM) og datadreven modellering (DDM) kombineres for å utvikle modeller som er generaliserbare, pålitelige, nøyaktige, ... -
Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions
Belay, Mohammed Ayalew; Blakseth, Sindre Stenen; Rasheed, Adil; Salvo Rossi, Pierluigi (Peer reviewed; Journal article, 2023)The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in ...