| Deep learning observables in computational fluid dynamics KO Lye, S Mishra, D Ray Journal of Computational Physics 410, 109339, 2020 | 161 | 2020 |
| Iterative surrogate model optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks KO Lye, S Mishra, D Ray, P Chandrashekar Computer Methods in Applied Mechanics and Engineering 374, 113575, 2021 | 61 | 2021 |
| A multi-level procedure for enhancing accuracy of machine learning algorithms KO Lye, S Mishra, R Molinaro European Journal of Applied Mathematics 32 (3), 436-469, 2021 | 34 | 2021 |
| Statistical solutions of hyperbolic systems of conservation laws: numerical approximation US Fjordholm, K Lye, S Mishra, F Weber Mathematical Models and Methods in Applied Sciences 30 (03), 539-609, 2020 | 27 | 2020 |
| Constraint preserving mixers for the quantum approximate optimization algorithm FG Fuchs, KO Lye, H Møll Nilsen, AJ Stasik, G Sartor Algorithms 15 (6), 202, 2022 | 26 | 2022 |
| Numerical approximation of statistical solutions of scalar conservation laws US Fjordholm, K Lye, S Mishra SIAM Journal on Numerical Analysis 56 (5), 2989-3009, 2018 | 16 | 2018 |
| Deep Ray, and Praveen Chandrashekar. Iterative surrogate model optimization (ismo): An active learning algorithm for pde constrained optimization with deep neural networks KO Lye, S Mishra Computer Methods in Applied Mechanics and Engineering 374, 113575, 2021 | 12 | 2021 |
| A framework for OpenGL client-server rendering C Dyken, KO Lye, J Seland, EW Bjønnes, J Hjelmervik, JO Nygaard, ... 4th IEEE International Conference on Cloud Computing Technology and Science …, 2012 | 12 | 2012 |
| Computation of statistical solutions of hyperbolic systems of conservation laws KO Lye ETH Zurich, 2020 | 10 | 2020 |
| Multilevel Monte-Carlo for measure valued solutions KO Lye arXiv preprint arXiv:1611.07732, 2016 | 7 | 2016 |
| Pseudo-Hamiltonian neural networks for learning partial differential equations S Eidnes, KO Lye Journal of Computational Physics 500, 112738, 2024 | 5 | 2024 |
| Convergence rates of monotone schemes for conservation laws for data with unbounded total variation US Fjordholm, KO Lye Journal of Scientific Computing 91 (2), 32, 2022 | 3 | 2022 |
| Constrained mixers for QAOA FG Fuchs, KO Lye, HM Nilsen, AJ Stasik, G Sartor arXiv preprint arXiv:2203.06095, 2022 | 2 | 2022 |
| On C*-algebras related to the Roe algebra KO Lye | 2 | 2011 |
| Alsvinn: A Fast multi-GPGPU finite volume solver with a strong emphasis on reproducibility K Lye arXiv preprint arXiv:1912.07645, 2019 | 1 | 2019 |
| Density-consistent initialization of SPH on a regular Cartesian grid: Comparative numerical study of 10 smoothing kernels in 1, 2 and 3 dimensions A Lavrov, P Skjetne, B Lund, E Bjønnes, FO Bjørnson, JO Busklein, ... Procedia IUTAM 18, 85-95, 2015 | 1 | 2015 |
| Multi-level data assimilation for simplified ocean models F Beiser, HH Holm, KO Lye, J Eidsvik Nonlinear Processes in Geophysics Discussions 2024, 1-33, 2024 | | 2024 |
| A Reinforcement Learning framework for Wake Steering of Wind Turbines KO Lye, MV Tabib, KA Johannessen Journal of Physics: Conference Series 2626 (1), 012051, 2023 | | 2023 |
| Statistical solutions of hyperbolic systems of conservation laws: numerical approximation U Skre Fjordholm, K Lye, S Mishra, F Weber arXiv e-prints, arXiv: 1906.02536, 2019 | | 2019 |
| Numerical approximation of statistical solutions of scalar conservation laws U Skre Fjordholm, K Lye, S Mishra arXiv e-prints, arXiv: 1710.11173, 2017 | | 2017 |
