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 long short term memory forhybrid uplifted reduced order models. arXiv.org.
- (2020) Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning. IEEE Access. vol. 8.
- (2020) Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles. arXiv.org.
- (2020) An evolve-then-correct reduced order model for hidden fluid dynamics. arXiv.org.
- (2020) Data-driven recovery of hiddenp hysics in reduced order modeling of fluid flows. Physics of Fluids.
- (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.
- (2019) Dissecting Deep Neural Networks. arXiv.org.
- (2019) Finite-Volume High-Fidelity Simulation Combined with Finite-Element-Based Reduced-Order Modeling of Incompressible Flow Problems. Energies. vol. 12 (7).
- (2019) High Fidelity Computational Fluid Dynamics Assessment of Wind Tunnel Turbine Test. Journal of Physics: Conference Series. vol. 1356 (1).
- (2019) Validation of the numerical simulations of flow around a scaled-down turbine using experimental data from wind tunnel. Wind and Structures. vol. 29 (6).
- (2019) Numerical investigation of modeling frameworks and geometric approximations on NREL 5 MW wind turbine. Renewable Energy. vol. 132.
- (2018) Data-driven deconvolution for large eddy simulations of Kraichnan turbulence. Physics of Fluids. vol. 30 (12).