Petr Karnakov

Publications

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]
[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,” in Proceedings of the platform for advanced scientific computing conference, in PASC ’19. Zurich, Switzerland, 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,” in Proceedings of the platform for advanced scientific computing conference, in PASC ’20. Geneva, Switzerland, 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.
[16]
M. Balcerak et al., “Physics-regularized multi-modal image assimilation for brain tumor localization,” in Advances in neural information processing systems, A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, Eds., Curran Associates, Inc., 2024, pp. 41909–41933. Available: https://proceedings.neurips.cc/paper_files/paper/2024/file/49fb58cfd482a33619d48a5c5910cf3c-Paper-Conference.pdf
[17]
M. Balcerak et al., “Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss,” Nature Communications, vol. 16, no. 1, p. 5982, 2025, doi: 10.1038/s41467-025-60366-4.
[18]
B. Buhendwa Aaron B. and P. Koumoutsakos, “Data-driven shape inference in three-dimensional steady-state supersonic flows: Optimizing a discrete loss with JAX-fluids,” Phys. Rev. Fluids, Jun. 2025, doi: 10.1103/9wj9-nmr8.
[19]
P. Karnakov, L. Amoudruz, and P. Koumoutsakos, “Optimal navigation in microfluidics via the optimization of a discrete loss,” Phys. Rev. Lett., vol. 134, p. 044001, Jan. 2025, doi: 10.1103/PhysRevLett.134.044001.
[20]
L. Amoudruz, P. Karnakov, and P. Koumoutsakos, “Contactless precision steering of particles in a fluid inside a cube with rotating walls,” Journal of Fluid Mechanics, vol. 1014, p. A15, 2025, doi: 10.1017/jfm.2025.10174.