Petr Karnakov

Publications

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
[1]
S. Cherny et al., “Simulating fully 3D non-planar evolution of hydraulic fractures,” International Journal of Fracture, vol. 201, no. 2, pp. 181–211, 2016, doi: 10.1007/s10704-016-0122-x.
[2]
P. Karnakov, D. Kuranakov, V. Lapin, S. Cherny, and D. Esipov, “Peculiarities of the hydraulic fracture propagation caused by pumping of proppant-fluid slurry,” Thermophysics and Aeromechanics, vol. 25, no. 4, pp. 587–603, 2018, doi: 10.1134/s086986431804011x.
[3]
S. M. H. Hashemi et al., “A versatile and membrane-less electrochemical reactor for the electrolysis of water and brine,” Energy & Environmental Science, vol. 12, no. 5, pp. 1592–1604, 2019, doi: 10.1039/c9ee00219g.
[4]
U. Rasthofer, F. Wermelinger, P. Karnakov, J. Šukys, and P. Koumoutsakos, “Computational study of the collapse of a cloud with 12 500 gas bubbles in a liquid,” Physical Review Fluids, vol. 4, no. 6, p. 063602, 2019, doi: 10.1103/PhysRevFluids.4.063602.
[5]
P. Karnakov, F. Wermelinger, M. Chatzimanolakis, S. Litvinov, and P. Koumoutsakos, “A high performance computing framework for multiphase, turbulent flows on structured grids,” 2019. doi: 10.1145/3324989.3325727.
[6]
P. Karnakov, F. Wermelinger, S. Litvinov, and P. Koumoutsakos, “Aphros: High performance software for multiphase flows with large scale bubble and drop clusters,” 2020. doi: 10.1145/3394277.3401856.
[7]
P. Karnakov, S. Litvinov, and P. Koumoutsakos, “A hybrid particle volume-of-fluid method for curvature estimation in multiphase flows,” International Journal of Multiphase Flow, vol. 125, p. 103209, 2020, doi: 10.1016/j.ijmultiphaseflow.2020.103209.
[8]
Z. Y. Wan, P. Karnakov, P. Koumoutsakos, and T. P. Sapsis, “Bubbles in turbulent flows: Data-driven, kinematic models with history terms,” International Journal of Multiphase Flow, vol. 129, p. 103286, 2020, doi: 10.1016/j.ijmultiphaseflow.2020.103286.
[9]
P. Karnakov et al., “Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries,” Swiss medical weekly, vol. 150, p. w20313, 2020, doi: 10.4414/smw.2020.20313.
[10]
P. Karnakov, S. Litvinov, J. M. Favre, and P. Koumoutsakos, “Breaking waves: To foam or not to foam?” Physical Review Fluids, vol. 5, no. 11, p. 110503, 2020, doi: 10.1103/PhysRevFluids.5.110503.
[11]
M. Chatzimanolakis et al., “Optimal allocation of limited test resources for the quantification of COVID-19 infections,” Swiss Medical Weekly, vol. 150, p. w20445, 2020, doi: 10.4414/smw.2020.20445.
[12]
S. M. Martin, D. Wälchli, G. Arampatzis, A. E. Economides, P. Karnakov, and P. Koumoutsakos, “Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 389, p. 114264, 2022, doi: 10.1016/j.cma.2021.114264.
[13]
P. Karnakov, S. Litvinov, and P. Koumoutsakos, “Computing foaming flows across scales: From breaking waves to microfluidics,” Science Advances, vol. 8, no. 5, p. eabm0590, 2022, doi: 10.1126/sciadv.abm0590.
[14]
P. Karnakov, S. Litvinov, and P. Koumoutsakos, “Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation,” The European Physical Journal E, vol. 46, no. 7, p. 59, 2023, doi: 10.1140/epje/s10189-023-00313-7.
[15]
P. Karnakov, S. Litvinov, and P. Koumoutsakos, “Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks,” PNAS Nexus, p. pgae005, Jan. 2024, doi: 10.1093/pnasnexus/pgae005.