• 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 ...
    • On the Piecewise Affine Representation of Neural Networks 

      Robinson, Haakon (Master thesis, 2019)
      Det kan vises at nevrale nett kan uttrykkes som stykkevise affine (PWA) funksjoner. Men, forskning har fokusert på å telle de lineære regionene, fremfor å finne den eksplitte PWA formen. Denne oppgaven fremfører en algoritme ...
    • Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments 

      Håkansson, Anne; Saad, Aya; Sadanandan Anand, Akhil; Gjærum, Vilde Benoni; Robinson, Haakon; Seel, Katrine (Peer reviewed; Journal article, 2021)
      Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, ...
    • Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review 

      Sadanandan Anand, Akhil; Seel, Katrine; Gjærum, Vilde Benoni; Håkansson, Anne; Robinson, Haakon; Saad, Aya (Peer reviewed; Journal article, 2021)
      Real-world autonomous systems are often controlled using conventional model-based control methods. But if accurate models of a system are not available, these methods may be unsuitable. For many safety-critical systems, ...
    • 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 ...
    • 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 ...