Blar i SINTEF Open på forfatter "Robinson, Haakon"

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    • GANs enabled super-resolution reconstruction of wind field 

      Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Tabib, Mandar; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)
      Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ...

    • GANs enabled super-resolution reconstruction of wind field 

      Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)
      Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ...

    • Physics guided neural networks for modelling of non-linear dynamics 

      Robinson, Haakon; Pawar, Suraj; Rasheed, Adil; San, Omer (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 ...

    • Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning 

      Meyer, Eivind; Robinson, Haakon; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2020)
      In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an ...