Blar i SINTEF Open på forfatter "Lye, Kjetil Olsen"

Viser treff 1-6 av 6

    • A Framework for OpenGL Client-Server Rendering 

      Dyken, Erik Christopher; Lye, Kjetil Olsen; Seland, Johan Simon; Bjønnes, Erik W; Hjelmervik, Jon M.; Nygaard, Jens Olav; Hagen, Trond Runar (Chapter, 2012)
      We present a software framework that facilitates the development of OpenGL applications utilizing the limited GPU capacities of a portable client in combination with the high-end rendering hardware on a server. The resulting ...

    • Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm 

      Fuchs, Franz Georg; Lye, Kjetil Olsen; Nilsen, Halvor Møll; Stasik, Alexander Johannes; Sartor, Giorgio (Peer reviewed; Journal article, 2022)
      The quantum approximate optimization algorithm/quantum alternating operator ansatz (QAOA) is a heuristic to find approximate solutions of combinatorial optimization problems. Most of the literature is limited to quadratic ...

    • Convergence Rates of Monotone Schemes for Conservation Laws for Data with Unbounded Total Variation 

      Fjordholm, Ulrik Skre; Lye, Kjetil Olsen (Peer reviewed; Journal article, 2022)
      We prove convergence rates of monotone schemes for conservation laws for Hölder continuous initial data with unbounded total variation, provided that the Hölder exponent of the initial data is greater than 12/. For strictly ...

    • Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks 

      Lye, Kjetil Olsen; Mishra, Siddhartha; Ray, Deep; Chandrasekhar, Praveen (Peer reviewed; Journal article, 2020)
      We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on ...

    • Pseudo-Hamiltonian neural networks for learning partial differential equations 

      Eidnes, Sølve; Lye, Kjetil Olsen (Peer reviewed; Journal article, 2024)
      Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning dynamical systems that can be modelled by ordinary differential equations. In this paper, we extend the method to partial differential equations. ...

    • A Reinforcement Learning framework for Wake Steering of Wind Turbines 

      Lye, Kjetil Olsen; Tabib, Mandar Vasudeo; Johannessen, Kjetil Andre (Peer reviewed; Journal article, 2023)
      Ideally, optimum power for a single turbine is obtained when the wind-turbine is aligned with the wind direction. However in multi-turbine wind-farm set-up, wake effects lead to decreased power production from downstream ...