Zobrazeno 1 - 10
of 261
pro vyhledávání: '"Larsson, Johan P"'
We introduce CaLES, a GPU-accelerated finite-difference solver designed for large-eddy simulations (LES) of incompressible wall-bounded flows in massively parallel environments. Built upon the existing direct numerical simulation (DNS) solver CaNS, C
Externí odkaz:
http://arxiv.org/abs/2411.09364
The impact of intrinsic compressibility effects -- changes in fluid volume due to pressure variations -- on high-speed wall-bounded turbulence has often been overlooked or incorrectly attributed to mean property variations. To unambiguously quantify
Externí odkaz:
http://arxiv.org/abs/2406.07649
Accurately predicting drag and heat transfer for compressible high-speed flows is of utmost importance for a range of engineering applications. This requires the precise knowledge of the entire velocity and temperature profiles. A common approach is
Externí odkaz:
http://arxiv.org/abs/2307.02199
A transformation that relates a compressible wall-bounded turbulent flow with non-uniform fluid properties to an equivalent incompressible flow with uniform fluid properties is derived and validated. The transformation accounts for both variable-prop
Externí odkaz:
http://arxiv.org/abs/2305.06712
Autor:
Pogliano, Francesco, Larsen, Ann-Cecilie, Goriely, Stephane, Siess, Lionel, Markova, Maria, Görgen, Andreas, Heines, Johannes, Ingeberg, Vetle Werner, Kjus, Robin Grongstad, Larsson, Johan Emil Linnestad, Li, Kevin Ching Wei, Martinsen, Elise Malmer, Owens-Fryar, Gerard Jordan, Pedersen, Line Gaard, Torvund, Gulla Serville, Tsantiri, Artemis
The $\gamma$-ray strength function and the nuclear level density of $^{167}$Ho have been extracted using the Oslo method from a $^{164}\text{Dy}(\alpha,p\gamma)^{167}$Ho experiment carried out at the Oslo Cyclotron Laboratory. The level density displ
Externí odkaz:
http://arxiv.org/abs/2304.14517
The accuracy and computational cost of a large eddy simulation are highly dependent on the computational grid. Building optimal grids manually from a priori knowledge is not feasible in most practical use cases; instead, solution-adaptive strategies
Externí odkaz:
http://arxiv.org/abs/2301.03199
Publikováno v:
Proceedings of the 26th international conference on artificial intelligence and statistics. Valencia, Spain: PMLR; 2023. p. 4802-21. (PMLR; vol. 206)
The lasso is the most famous sparse regression and feature selection method. One reason for its popularity is the speed at which the underlying optimization problem can be solved. Sorted L-One Penalized Estimation (SLOPE) is a generalization of the l
Externí odkaz:
http://arxiv.org/abs/2210.14780
Autor:
Moreau, Thomas, Massias, Mathurin, Gramfort, Alexandre, Ablin, Pierre, Bannier, Pierre-Antoine, Charlier, Benjamin, Dagréou, Mathieu, la Tour, Tom Dupré, Durif, Ghislain, Dantas, Cassio F., Klopfenstein, Quentin, Larsson, Johan, Lai, En, Lefort, Tanguy, Malézieux, Benoit, Moufad, Badr, Nguyen, Binh T., Rakotomamonjy, Alain, Ramzi, Zaccharie, Salmon, Joseph, Vaiter, Samuel
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: rese
Externí odkaz:
http://arxiv.org/abs/2206.13424
Autor:
Larsson, Johan
Publikováno v:
22nd European young statisticians meeting - proceedings (eds. Makridis, A., Milienos et al.) 61-65 (Panteion university of social and political sciences, Athens, Greece, 2021)
The lasso is a popular method to induce shrinkage and sparsity in the solution vector (coefficients) of regression problems, particularly when there are many predictors relative to the number of observations. Solving the lasso in this high-dimensiona
Externí odkaz:
http://arxiv.org/abs/2105.05648
Autor:
Larsson, Johan, Wallin, Jonas
Publikováno v:
Advances in neural information processing systems 35 (eds. Koyejo, S. et al.) vol. 35 15823-15835 (Curran Associates, Inc., New Orleans, USA, 2022)
Predictor screening rules, which discard predictors before fitting a model, have had considerable impact on the speed with which sparse regression problems, such as the lasso, can be solved. In this paper we present a new screening rule for solving t
Externí odkaz:
http://arxiv.org/abs/2104.13026