Bakgrunn og aktiviteter
- Bigdata Cybernetics: Combining data-driven methods with control theory
- Hybrid Analytics / Modeling: Combining physics based modeling with advanced machine learning algorithms
- Artificial Intelligence and Machine Learning
- Reduced Order Modeling
- Computational Fluid Dynamics and Turbulence Modelling
- Numerical Methods
- Wind Energy
- Autonomous Vessels
- Drones in urban area
Vitenskapelig, faglig og kunstnerisk arbeid
Et utvalg av nyere tidsskriftspublikasjoner, kunstneriske produksjoner, bok, inklusiv bokdeler og rapport-del. Se alle publikasjoner i databasen
- (2020) A forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction. arXiv.org.
- (2020) Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning. arXiv.org.
- (2020) Interface learning of multiphysics and multiscale systems. arXiv.org.
- (2020) A long short term memory forhybrid uplifted reduced order models. Physica D : Non-linear phenomena. vol. 409.
- (2020) Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning. IEEE Access. vol. 8.
- (2020) Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles. arXiv.org.
- (2020) Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles. arXiv.org.
- (2020) COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning. arXiv.org.
- (2020) An evolve-then-correct reduced order model for hidden fluid dynamics. Mathematics. vol. 8(4) (570).
- (2020) Data-driven recovery of hiddenp hysics in reduced order modeling of fluid flows. Physics of Fluids.
- (2020) Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows. arXiv.org.
- (2020) A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence. Theoretical and Computational Fluid Dynamics.
- (2020) Digital Twin: Values, Challenges and Enablers from a modelling perspective. IEEE Access. vol. 8.
- (2020) Numerical assessment of RANS turbulence models for the development ofdata driven Reduced Order Models. Ocean Engineering. vol. 196 (106799).
- (2020) Marine life through You Only Look Once's perspective. arXiv.org.
- (2020) Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data. Physics of Fluids. vol. 32 (015113).
- (2019) Memory embedded non-intrusive reduced order modeling of non-ergodic flows. Physics of Fluids. vol. 31 (12).
- (2019) Fast divergence-conforming reduced basis methods for steady Navier–Stokes flow. Computer Methods in Applied Mechanics and Engineering. vol. 346.
- (2019) A deep learning enabler for non-intrusive reduced order modeling of fluid flows. Physics of Fluids. vol. 31 (8).
- (2019) Nonintrusive reduced order modeling framework for quasigeostrophic turbulence. Physical review. E. vol. 100 (5).