1<!DOCTYPE html> 2<html xmlns="http://www.w3.org/1999/xhtml"> 3<head> 4 <meta charset="utf-8" /> 5 <meta name="generator" content="pandoc 3.10.2" /> 6 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> 7 <title>Petr Karnakov</title> 8 <style> 9 /* Default styles provided by pandoc. 10 ** See https://pandoc.org/MANUAL.html#variables-for-html for config info. 11 */ 12 span.smallcaps{font-variant: small-caps;} 13 div.columns{display: flex; gap: 1.5em;} 14 div.column{flex: auto;} 15 @media screen { 16 div.columns{gap: min(4vw, 1.5em);} 17 div.column{overflow-x: auto;} 18 } 19 div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} 20 /* The extra [class] is a hack that increases specificity enough to 21 override a similar rule in reveal.js */ 22 ul.task-list[class]{list-style: none;} 23 ul.task-list li input[type="checkbox"] { 24 font-size: inherit; 25 width: 0.8em; 26 margin: 0 0.8em 0.2em -1.6em; 27 vertical-align: middle; 28 } 29 .display.math{display: block; text-align: center; margin: 0.5rem auto;} 30 /* CSS for citations */ 31 div.csl-bib-body { } 32 div.csl-entry { 33 clear: both; 34 margin-bottom: 0em; 35 } 36 .hanging-indent div.csl-entry { 37 margin-left:2em; 38 text-indent:-2em; 39 } 40 div.csl-left-margin { 41 min-width:2em; 42 float:left; 43 } 44 div.csl-right-inline { 45 margin-left:2em; 46 padding-left:1em; 47 } 48 div.csl-indent { 49 margin-left: 2em; 50 } </style> 51 <link rel="stylesheet" href="style.css?hash=efcc6" /> 52 <link rel="icon" type="image/png" href="media/favicon.png" /> 53</head> 54<body> 55<h1 id="petr-karnakov"><a href="./" class="nocolor">Petr 56Karnakov</a></h1> 57<h2 id="publications">Publications</h2> 58<div style="display:none;"> 59<span class="citation" data-cites="cherny2016simulating">[1]</span> 60<span class="citation" data-cites="karnakov2018peculiarities">[2]</span> 61<span class="citation" data-cites="hashemi2019versatile">[3]</span> 62<span class="citation" 63data-cites="rasthofer2019computational">[4]</span> <span 64class="citation" data-cites="karnakov2019high">[5]</span> <span 65class="citation" data-cites="karnakov2020aphros">[6]</span> <span 66class="citation" data-cites="karnakov2020hybrid">[7]</span> <span 67class="citation" data-cites="wan2020bubbles">[8]</span> <span 68class="citation" data-cites="karnakov2020data">[9]</span> <span 69class="citation" data-cites="karnakov2020breaking">[10]</span> <span 70class="citation" data-cites="chatzimanolakis2020optimal">[11]</span> 71<span class="citation" data-cites="martin2022korali">[12]</span> <span 72class="citation" data-cites="karnakov2022computing">[13]</span> <span 73class="citation" data-cites="karnakov2023flow">[14]</span> <span 74class="citation" data-cites="karnakov2024odil">[15]</span> <span 75class="citation" data-cites="balcerak2024physics">[16]</span> <span 76class="citation" data-cites="balcerak2025individualizing">[17]</span> 77<span class="citation" data-cites="buhendwa2025datadriven">[18]</span> 78<span class="citation" data-cites="karnakov2025optimal">[19]</span> 79<span class="citation" data-cites="amoudruz2025contactless">[20]</span> 80</div> 81<div id="refs" class="references csl-bib-body" data-entry-spacing="0" 82role="list"> 83<div id="ref-cherny2016simulating" class="csl-entry" role="listitem"> 84<div class="csl-left-margin">[1] </div><div class="csl-right-inline">S. 85Cherny <em>et al.</em>, <span>âSimulating fully <span>3D</span> 86non-planar evolution of hydraulic fractures,â</span> <em>International 87Journal of Fracture</em>, vol. 201, no. 2, pp. 181â211, 2016, doi: <a 88href="https://doi.org/10.1007/s10704-016-0122-x">10.1007/s10704-016-0122-x</a>.</div> 89</div> 90<div id="ref-karnakov2018peculiarities" class="csl-entry" 91role="listitem"> 92<div class="csl-left-margin">[2] </div><div class="csl-right-inline">P. 93Karnakov, D. Kuranakov, V. Lapin, S. Cherny, and D. Esipov, 94<span>âPeculiarities of the hydraulic fracture propagation caused by 95pumping of proppant-fluid slurry,â</span> <em>Thermophysics and 96Aeromechanics</em>, vol. 25, no. 4, pp. 587â603, 2018, doi: <a 97href="https://doi.org/10.1134/s086986431804011x">10.1134/s086986431804011x</a>.</div> 98</div> 99<div id="ref-hashemi2019versatile" class="csl-entry" role="listitem"> 100<div class="csl-left-margin">[3] </div><div class="csl-right-inline">S. 101M. H. Hashemi <em>et al.</em>, <span>âA versatile and membrane-less 102electrochemical reactor for the electrolysis of water and brine,â
102</span> 103<em>Energy & Environmental Science</em>, vol. 12, no. 5, pp. 1041592â1604, 2019, doi: <a 105href="https://doi.org/10.1039/c9ee00219g">10.1039/c9ee00219g</a>.</div> 106</div> 107<div id="ref-rasthofer2019computational" class="csl-entry" 108role="listitem"> 109<div class="csl-left-margin">[4] </div><div class="csl-right-inline">U. 110Rasthofer, F. Wermelinger, P. Karnakov, J. Å ukys, and P. Koumoutsakos, 111<span>âComputational study of the collapse of a cloud with 12 500 gas 112bubbles in a liquid,â</span> <em>Physical Review Fluids</em>, vol. 4, 113no. 6, p. 063602, 2019, doi: <a 114href="https://doi.org/10.1103/PhysRevFluids.4.063602">10.1103/PhysRevFluids.4.063602</a>.</div> 115</div> 116<div id="ref-karnakov2019high" class="csl-entry" role="listitem"> 117<div class="csl-left-margin">[5] </div><div class="csl-right-inline">P. 118Karnakov, F. Wermelinger, M. Chatzimanolakis, S. Litvinov, and P. 119Koumoutsakos, <span>âA high performance computing framework for 120multiphase, turbulent flows on structured grids,â</span> in 121<em>Proceedings of the platform for advanced scientific computing 122conference</em>, in PASC â19. Zurich, Switzerland, 2019. doi: <a 123href="https://doi.org/10.1145/3324989.3325727">10.1145/3324989.3325727</a>.</div> 124</div> 125<div id="ref-karnakov2020aphros" class="csl-entry" role="listitem"> 126<div class="csl-left-margin">[6] </div><div class="csl-right-inline">P. 127Karnakov, F. Wermelinger, S. Litvinov, and P. Koumoutsakos, 128<span>âAphros: High performance software for multiphase flows with large 129scale bubble and drop clusters,â</span> in <em>Proceedings of the 130platform for advanced scientific computing conference</em>, in PASC â20. 131Geneva, Switzerland, 2020. doi: <a 132href="https://doi.org/10.1145/3394277.3401856">10.1145/3394277.3401856</a>.</div> 133</div> 134<div id="ref-karnakov2020hybrid" class="csl-entry" role="listitem"> 135<div class="csl-left-margin">[7] </div><div class="csl-right-inline">P. 136Karnakov, S. Litvinov, and P. Koumoutsakos, <span>âA hybrid particle 137volume-of-fluid method for curvature estimation in multiphase 138flows,â</span> <em>International Journal of Multiphase Flow</em>, vol. 139125, p. 103209, 2020, doi: <a 140href="https://doi.org/10.1016/j.ijmultiphaseflow.2020.103209">10.1016/j.ijmultiphaseflow.2020.103209</a>.</div> 141</div> 142<div id="ref-wan2020bubbles" class="csl-entry" role="listitem"> 143<div class="csl-left-margin">[8] </div><div class="csl-right-inline">Z. 144Y. Wan, P. Karnakov, P. Koumoutsakos, and T. P. Sapsis, <span>âBubbles 145in turbulent flows: Data-driven, kinematic models with history 146terms,â</span> <em>International Journal of Multiphase Flow</em>, vol. 147129, p. 103286, 2020, doi: <a 148href="https://doi.org/10.1016/j.ijmultiphaseflow.2020.103286">10.1016/j.ijmultiphaseflow.2020.103286</a>.</div> 149</div> 150<div id="ref-karnakov2020data" class="csl-entry" role="listitem"> 151<div class="csl-left-margin">[9] </div><div class="csl-right-inline">P. 152Karnakov <em>et al.</em>, <span>âData-driven inference of the 153reproduction number for <span>COVID</span>-19 before and after 154interventions for 51 <span>European</span> countries,â</span> <em>Swiss 155medical weekly</em>, vol. 150, p. w20313, 2020, doi: <a 156href="https://doi.org/10.4414/smw.2020.20313">10.4414/smw.2020.20313</a>.</div> 157</div> 158<div id="ref-karnakov2020breaking" class="csl-entry" role="listitem"> 159<div class="csl-left-margin">[10] </div><div class="csl-right-inline">P. 160Karnakov, S. Litvinov, J. M. Favre, and P. Koumoutsakos, <span>âBreaking 161waves: To foam or not to foam?â</span> <em>Physical Review Fluids</em>, 162vol. 5, no. 11, p. 110503, 2020, doi: <a 163href="https://doi.org/10.1103/PhysRevFluids.5.110503">10.1103/PhysRevFluids.5.110503</a>.</div> 164</div> 165<div id="ref-chatzimanolakis2020optimal" class="csl-entry" 166role="listitem"> 167<div class="csl-left-margin">[11] </div><div class="csl-right-inline">M. 168Chatzimanolakis <em>et al.</em>, <span>âOptimal allocation of limited 169test resources for the quantification of <span>COVID</span>-19 170infections,â</span> <em>Swiss Medical Weekly</em>, vol. 150, p. w20445, 1712020, doi: <a 172href="https://doi.org/10.4414/smw.2020.20445">10.4414/smw.2020.20445</a>.</div> 173</div> 174<div id="ref-martin2022korali" class="csl-entry" role="listitem"> 175<div class="csl-left-margin">[12] </div><div class="csl-right-inline">S. 176M. Martin, D. Wälchli, G. Arampatzis, A. E. Economides, P. Karnakov, and 177P. Koumoutsakos, <span>âKorali: Efficient and scalable software 178framework for <span>Bayesian</span> uncertainty quantification and 179stochastic optimization,â</span> <em>Computer Methods in Applied 180Mechanics and Engineering</em>, vol. 389, p. 114264, 2022, doi: <a 181href="https://doi.org/10.1016/j.cma.2021.114264">10.1016/j.cma.2021.114264</a>.</div> 182</div> 183<div id="ref-karnakov2022computing" class="csl-entry" role="listitem"> 184<div class="csl-left-margin">[13] </div><div class="csl-right-inline">P. 185Karnakov, S. Litvinov, and P. Koumoutsakos, <span>âComputing foaming 186flows across scales: From breaking waves to microfluidics,â</span> 187<em>Science Advances</em>, vol. 8, no. 5, p. eabm0590, 2022, doi: <a 188href="https://doi.org/10.1126/sciadv.abm0590">10.1126/sciadv.abm0590</a>.</div> 189</div> 190<div id="ref-karnakov2023flow" class="csl-entry" role="listitem"> 191<div class="csl-left-margin">[14] </div><div class="csl-right-inline">P. 192Karnakov, S. Litvinov, and P. Koumoutsakos, <span>âFlow reconstruction 193by multiresolution optimization of a discrete loss with automatic 194differentiation,â</span> <em>The European Physical Journal E</em>, vol. 19546, no. 7, p. 59, 2023, doi: <a 196href="https://doi.org/10.1140/epje/s10189-023-00313-7">10.1140/epje/s10189-023-00313-7</a>.</div> 197</div> 198<div id="ref-karnakov2024odil" class="csl-entry" role="listitem"> 199<div class="csl-left-margin">[15] </div><div class="csl-right-inline">P. 200Karnakov, S. Litvinov, and P. Koumoutsakos, <span>â<span 201class="nocase">Solving inverse problems in physics by optimizing a 202discrete loss: Fast and accurate learning without neural 203networks</span>,â</span> <em>PNAS Nexus</em>, p. pgae005, Jan. 2024, 204doi: <a 205href="https://doi.org/10.1093/pnasnexus/pgae005">10.1093/pnasnexus/pgae005</a>.</div> 206</div> 207<div id="ref-balcerak2024physics" class="csl-entry" role="listitem"> 208<div class="csl-left-margin">[16] </div><div class="csl-right-inline">M. 209Balcerak <em>et al.</em>, <span>âPhysics-regularized multi-modal image 210assimilation for brain tumor localization,â</span> in <em>Advances in 211neural information processing systems</em>, A. Globerson, L. Mackey, D. 212Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, Eds., Curran 213Associates, Inc., 2024, pp. 41909â41933. Available: <a 214href="https://proceedings.neurips.cc/paper_files/paper/2024/file/49fb58cfd482a33619d48a5c5910cf3c-Paper-Conference.pdf">https://proceedings.neurips.cc/paper_files/paper/2024/file/49fb58cfd482a33619d48a5c5910cf3c-Paper-Conference.pdf</a></div> 215</div> 216<div id="ref-balcerak2025individualizing" class="csl-entry" 217role="listitem"> 218<div class="csl-left-margin">[17] </div><div 219class="csl-right-inline"><span class="nocase">M. Balcerak <em>et 220al.</em></span>, <span>âIndividualizing glioma radiotherapy planning by 221optimization of a data and physics-informed discrete loss,â</span> 222<em>Nature Communications</em>, vol. 16, no. 1, p. 5982, 2025, doi: <a 223href="https://doi.org/10.1038/s41467-025-60366-4">10.1038/s41467-025-60366-4</a>.</div> 224</div> 225<div id="ref-buhendwa2025datadriven" class="csl-entry" role="listitem"> 226<div class="csl-left-margin">[18] </div><div class="csl-right-inline">B. 227Buhendwa Aaron B. and P. Koumoutsakos, <span>âData-driven shape 228inference in three-dimensional steady-state supersonic flows: Optimizing 229a discrete loss with JAX-fluids,â</span> <em>Phys. Rev. Fluids</em>, 230June 2025, doi: <a 231href="https://doi.org/10.1103/9wj9-nmr8">10.1103/9wj9-nmr8</a>.</div> 232</div> 233<div id="ref-karnakov2025optimal" class="csl-entry" role="listitem"> 234<div class="csl-left-margin">[19] </div><div class="csl-right-inline">P. 235Karnakov, L. Amoudruz, and P. Koumoutsakos, <span>âOptimal navigation in 236microfluidics via the optimization of a discrete loss,â</span> <em>Phys. 237Rev. Lett.</em>, vol. 134, p. 044001, Jan. 2025, doi: <a 238href="https://doi.org/10.1103/PhysRevLett.134.044001">10.1103/PhysRevLett.134.044001</a>.</div> 239</div> 240<div id="ref-amoudruz2025contactless" class="csl-entry" role="listitem"> 241<div class="csl-left-margin">[20] </div><div class="csl-right-inline">L. 242Amoudruz, P. Karnakov, and P. Koumoutsakos, <span>âContactless precision 243steering of particles in a fluid inside a cube with rotating 244walls,â</span> <em>Journal of Fluid Mechanics</em>, vol. 1014, p. A15, 2452025, doi: <a 246href="https://doi.org/10.1017/jfm.2025.10174">10.1017/jfm.2025.10174</a>.</div> 247</div> 248</div> 249</body> 250</html>
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