In late December 2019, the World Health Organization (WHO) announced the outbreak of a new type of coronavirus, named the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The deadliness of the virus has forced governments and countries to socially isolate their populations, causing a worldwide impact on the economy. Pandemic management has stressed health systems to work beyond their limits, adding more to the tragedy of losing millions of lives. As a natural response to such disasters, intelligent systems have been developed for various reasons related to virus detection, tracking and control. The social lockdown created a record level of online platforms and applications being used to resume professional and educational activities in a virtual environment. This has triggered an unprecedented growth in cybercrime. This paper presents the effects of the pandemic on computational intelligence and cybersecurity.
Citation: Mohamed Wiem Mkaouer, Tarek Gaber, and Zaineb Chelly Dagdia. Effects of COVID-19 pandemic on computational intelligence and cybersecurity: Survey[J]. Applied Computing and Intelligence, 2022, 2(2): 173-194. doi: 10.3934/aci.2022010
[1] | Rinaldo M. Colombo, Mauro Garavello . A Well Posed Riemann Problem for the p--System at a Junction. Networks and Heterogeneous Media, 2006, 1(3): 495-511. doi: 10.3934/nhm.2006.1.495 |
[2] | Yannick Holle, Michael Herty, Michael Westdickenberg . New coupling conditions for isentropic flow on networks. Networks and Heterogeneous Media, 2020, 15(4): 605-631. doi: 10.3934/nhm.2020016 |
[3] | Gabriella Bretti, Roberto Natalini, Benedetto Piccoli . Numerical approximations of a traffic flow model on networks. Networks and Heterogeneous Media, 2006, 1(1): 57-84. doi: 10.3934/nhm.2006.1.57 |
[4] | Jens Lang, Pascal Mindt . Entropy-preserving coupling conditions for one-dimensional Euler systems at junctions. Networks and Heterogeneous Media, 2018, 13(1): 177-190. doi: 10.3934/nhm.2018008 |
[5] | Michael Herty, Niklas Kolbe, Siegfried Müller . Central schemes for networked scalar conservation laws. Networks and Heterogeneous Media, 2023, 18(1): 310-340. doi: 10.3934/nhm.2023012 |
[6] | Samitha Samaranayake, Axel Parmentier, Ethan Xuan, Alexandre Bayen . A mathematical framework for delay analysis in single source networks. Networks and Heterogeneous Media, 2017, 12(1): 113-145. doi: 10.3934/nhm.2017005 |
[7] | Jan Friedrich, Simone Göttlich, Annika Uphoff . Conservation laws with discontinuous flux function on networks: a splitting algorithm. Networks and Heterogeneous Media, 2023, 18(1): 1-28. doi: 10.3934/nhm.2023001 |
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[9] | Michael Herty, J.-P. Lebacque, S. Moutari . A novel model for intersections of vehicular traffic flow. Networks and Heterogeneous Media, 2009, 4(4): 813-826. doi: 10.3934/nhm.2009.4.813 |
[10] | Gunhild A. Reigstad . Numerical network models and entropy principles for isothermal junction flow. Networks and Heterogeneous Media, 2014, 9(1): 65-95. doi: 10.3934/nhm.2014.9.65 |
In late December 2019, the World Health Organization (WHO) announced the outbreak of a new type of coronavirus, named the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The deadliness of the virus has forced governments and countries to socially isolate their populations, causing a worldwide impact on the economy. Pandemic management has stressed health systems to work beyond their limits, adding more to the tragedy of losing millions of lives. As a natural response to such disasters, intelligent systems have been developed for various reasons related to virus detection, tracking and control. The social lockdown created a record level of online platforms and applications being used to resume professional and educational activities in a virtual environment. This has triggered an unprecedented growth in cybercrime. This paper presents the effects of the pandemic on computational intelligence and cybersecurity.
[1] |
R. S. Istepanian, T. AlAnzi, Mobile health (m-health): Evidence-based progress or scientific retrogression, Biomedical Information Technology, (2020), 717–733. https://doi.org/10.1016/B978-0-12-816034-3.00022-5 doi: 10.1016/B978-0-12-816034-3.00022-5
![]() |
[2] |
C. D. Tran, T. T. Nguyen, Health vs. privacy? the risk-risk tradeoff in using covid-19 contact-tracing apps, Technol. Soc., 67 (2021), 101755. https://doi.org/10.1016/j.techsoc.2021.101755 doi: 10.1016/j.techsoc.2021.101755
![]() |
[3] | J. L. Boyles, A. Smith, M. Madden, Apps and privacy: More than half of app users have uninstalled or decided to not install an app due to concerns about their personal information, 2015. |
[4] | N. A. Khan, S. N. Brohi, N. Zaman, Ten deadly cyber security threats amid covid-19 pandemic, 2020. |
[5] | A. S. John, It's not just zoom. Google meet, Microsoft teams, and webex have privacy issues too, 2020. Consumer Reports. |
[6] |
A. R. Brough, K. D. Martin, Consumer privacy during (and after) the covid-19 pandemic, J. Public Policy Mark., 40 (2021), 108–110. https://doi.org/10.1177/0743915620929999 doi: 10.1177/0743915620929999
![]() |
[7] | M. Burgess, Hackers are targeting hospitals crippled by coronavirus, 2020. WIRED. |
[8] | N. C. S. C. Advisory, Covid-19 exploited by malicious cyber actors, 2021. |
[9] | S. Morgan, Cybercrime to cost the world fanxiexian_myfh10.5 trillion annually by 2025, Cybercrime Magazine, 13 (2020). |
[10] | D. L. Shinder, M. Cross, Scene of the Cybercrime, Elsevier, 2008. |
[11] |
H. S. Lallie, L. A. Shepherd, J. R. Nurse, A. Erola, G. Epiphaniou, C. Maple, et al., Cyber security in the age of Covid-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic, Comput. Secur., 105 (2021), 102248. https://doi.org/10.1016/j.cose.2021.102248 doi: 10.1016/j.cose.2021.102248
![]() |
[12] | J. R. C. Nurse, Cybercrime and you: How criminals attack and the human factors that they seek to exploit, 2018. https://doi.org/10.1093/oxfordhb/9780198812746.013.35 |
[13] | K. Tysiac, How cybercriminals prey on victims of natural disasters, 2020. |
[14] | E. Elsworthy, Hundreds of bushfire donation scams circulating, 2020. |
[15] | T. Foltýn, You have not won! a look at fake fifa world cup-themed lotteries and giveaways, 2018. |
[16] | NHS, 9 tips to help if you are worried about covid-19, 2020. |
[17] | P. D. las Cuevas, P. García-Sánchez, Z. Chelly Dagdia, M. I. García-Arenas, J. J. Merelo Guervós, Automatic rule extraction from access rules using genetic programming, In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), (2020), 54–69. Springer. https://doi.org/10.1007/978-3-030-43722-0_4 |
[18] | CGI, Helping defend against a 30,000% increase in phishing attacks related to covid-19 scams, 2020. |
[19] | INTERPOL, Interpol report shows alarming rate of cyberattacks during covid-19, 2020. |
[20] | J. Davis, Covid-19 impact on ransomware, threats, healthcare cybersecurity, 2020. |
[21] | J. Davis, Google blocking 18m coronavirus scam emails every day, 2020. |
[22] | Statista, Where do it professionals see an increase in cyber attacks and attack attempts following the covid-19 pandemic?, 2021. |
[23] | M. Obiso, I. Neto, M. Baayen, How tailored national cybersecurity strategies enable safe, inclusive and sustainable digital development, 2022. |
[24] | Global Cybersecurity Index, Global cybersecurity index 2020, 2020. |
[25] | NCS Guide 2021, 2nd edition of the guide to developing a national cybersecurity strategy, 2021. |
[26] |
G. Iakovakis, C. G. Xarhoulacos, K. Giovas, D. Gritzalis, Analysis and classification of mitigation tools against cyberattacks in covid-19 era, Secur. Commun. Netw., 2021 (2021), 3187205. https://doi.org/10.1155/2021/3187205 doi: 10.1155/2021/3187205
![]() |
[27] | Yahoo.com., Global virtual private network (vpn) markets report 2022, 2022. |
[28] | J. Joe, Safely scaling virtual private network for a major telecom company during a pandemic, Available at SSRN, (2022). |
[29] | marketsandmarkets.com., Multi-factor authentication market (2022 - 2026), 2022. |
[30] |
A. Y. F. Alsahlani, A. Popa, Lmaas-iot: Lightweight multi-factor authentication and authorization scheme for real-time data access in iot cloud-based environment, J. Netw. Comput. Appl., 192 (2021), 103177. https://doi.org/10.1016/j.jnca.2021.103177 doi: 10.1016/j.jnca.2021.103177
![]() |
[31] |
D. Vargo, L. Zhu, B. Benwell, Z. Yan, Digital technology use during covid-19 pandemic: A rapid review, Human Behavior and Emerging Technologies, 3 (2021), 13–24. https://doi.org/10.1002/hbe2.242 doi: 10.1002/hbe2.242
![]() |
[32] |
J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-ncov outbreak originating in wuhan, china: a modelling study, The Lancet, 395 (2020), 689–697. https://doi.org/10.1016/S0140-6736(20)30260-9 doi: 10.1016/S0140-6736(20)30260-9
![]() |
[33] |
S. Roy, W. Menapace, S. Oei, B. Luijten, E. Fini, C. Saltori, et al., Deep learning for classification and localization of covid-19 markers in point-of-care lung ultrasound, IEEE T. Med. Imaging, 39 (2020), 2676–2687. https://doi.org/10.1109/TMI.2020.2994459 doi: 10.1109/TMI.2020.2994459
![]() |
[34] |
L. Wang, Z. Q. Lin, A. Wong, Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images, Scientific Reports, 10 (2020), 1–12. https://doi.org/10.1038/s41598-020-76550-z doi: 10.1038/s41598-020-76550-z
![]() |
[35] |
H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez, V. Singh, Application of deep learning for fast detection of covid-19 in x-rays using ncovnet, Chaos, Solitons & Fractals, 138 (2020), 109944. https://doi.org/10.1016/j.chaos.2020.109944 doi: 10.1016/j.chaos.2020.109944
![]() |
[36] |
S. Wang, Y. Zha, W. Li, Q. Wu, X. Li, M. Niu, et al., A fully automatic deep learning system for covid-19 diagnostic and prognostic analysis, Eur. Respir. J., 56 (2020), 2000775. https://doi.org/10.1183/13993003.00775-2020 doi: 10.1183/13993003.00775-2020
![]() |
[37] |
Y. Oh, S. Park, J. C. Ye, Deep learning covid-19 features on cxr using limited training data sets, IEEE T. Med. Imaging, 39 (2020), 2688–2700. https://doi.org/10.1109/TMI.2020.2993291 doi: 10.1109/TMI.2020.2993291
![]() |
[38] |
S. Vaid, R. Kalantar, M. Bhandari, Deep learning covid-19 detection bias: accuracy through artificial intelligence, Int. Orthop., 44 (2020), 1539–1542. https://doi.org/10.1007/s00264-020-04609-7 doi: 10.1007/s00264-020-04609-7
![]() |
[39] |
D. Singh, V. Kumar, Vaishali, M. Kaur, Classification of covid-19 patients from chest ct images using multi-objective differential evolution–based convolutional neural networks, Eur. J. Clin. Microbiol., 39 (2020), 1379–1389. https://doi.org/10.1007/s10096-020-03901-z doi: 10.1007/s10096-020-03901-z
![]() |
[40] | L. Bai, D. Yang, X. Wang, L. Tong, X. Zhu, N. Zhong, et al., Chinese experts' consensus on the internet of things-aided diagnosis and treatment of coronavirus disease 2019 (covid-19), Clinical eHealth, 3 (2020), 7–15. |
[41] |
V. Chamola, V. Hassija, V. Gupta, M. Guizani, A comprehensive review of the covid-19 pandemic and the role of iot, drones, ai, blockchain, and 5g in managing its impact, Ieee access, 8 (2020), 90225–90265. https://doi.org/10.1109/ACCESS.2020.2992341 doi: 10.1109/ACCESS.2020.2992341
![]() |
[42] |
Z. Han, B. Wei, Y. Hong, T. Li, J. Cong, X. Zhu, et al., Accurate screening of covid-19 using attention-based deep 3d multiple instance learning, IEEE T. Med. Imaging, 39 (2020), 2584–2594. https://doi.org/10.1109/TMI.2020.2996256 doi: 10.1109/TMI.2020.2996256
![]() |
[43] |
D. C. Nguyen, P. N. Pathirana, M. Ding, A. Seneviratne, Blockchain for 5g and beyond networks: A state of the art survey, J. Netw. Comput. Appl., 166 (2020), 102693. https://doi.org/10.1016/j.jnca.2020.102693 doi: 10.1016/j.jnca.2020.102693
![]() |
[44] |
S. K. Lo, X. Xu, M. Staples, L. Yao, Reliability analysis for blockchain oracles, Comput. Electr. Eng., 83 (2020), 106582. https://doi.org/10.1016/j.compeleceng.2020.106582 doi: 10.1016/j.compeleceng.2020.106582
![]() |
[45] |
Z. C. Dagdia, A. C. S. e Silva, Effects of covid-19 pandemic on education and society, STEM Education, 2 (2022), 197–220. https://doi.org/10.3934/steme.2022013 doi: 10.3934/steme.2022013
![]() |
1. | R. M. Colombo, M. Herty, V. Sachers, On 2×2 Conservation Laws at a Junction, 2008, 40, 0036-1410, 605, 10.1137/070690298 | |
2. | BENJAMIN BOUTIN, CHRISTOPHE CHALONS, PIERRE-ARNAUD RAVIART, EXISTENCE RESULT FOR THE COUPLING PROBLEM OF TWO SCALAR CONSERVATION LAWS WITH RIEMANN INITIAL DATA, 2010, 20, 0218-2025, 1859, 10.1142/S0218202510004817 | |
3. | Alfredo Bermúdez, Xián López, M. Elena Vázquez-Cendón, Reprint of: Finite volume methods for multi-component Euler equations with source terms, 2018, 169, 00457930, 40, 10.1016/j.compfluid.2018.03.057 | |
4. | M. Herty, J. Mohring, V. Sachers, A new model for gas flow in pipe networks, 2010, 33, 01704214, 845, 10.1002/mma.1197 | |
5. | RINALDO M. COLOMBO, PAOLA GOATIN, BENEDETTO PICCOLI, ROAD NETWORKS WITH PHASE TRANSITIONS, 2010, 07, 0219-8916, 85, 10.1142/S0219891610002025 | |
6. | Jochen Kall, Rukhsana Kausar, Stephan Trenn, Modeling water hammers via PDEs and switched DAEs with numerical justification, 2017, 50, 24058963, 5349, 10.1016/j.ifacol.2017.08.927 | |
7. | Michael Herty, Coupling Conditions for Networked Systems of Euler Equations, 2008, 30, 1064-8275, 1596, 10.1137/070688535 | |
8. | Kristen DeVault, Pierre A. Gremaud, Vera Novak, Mette S. Olufsen, Guillaume Vernières, Peng Zhao, Blood Flow in the Circle of Willis: Modeling and Calibration, 2008, 7, 1540-3459, 888, 10.1137/07070231X | |
9. | CIRO D'APICE, BENEDETTO PICCOLI, VERTEX FLOW MODELS FOR VEHICULAR TRAFFIC ON NETWORKS, 2008, 18, 0218-2025, 1299, 10.1142/S0218202508003042 | |
10. | Stephan Gerster, Michael Herty, Michael Chertkov, Marc Vuffray, Anatoly Zlotnik, 2019, Chapter 8, 978-3-030-27549-5, 59, 10.1007/978-3-030-27550-1_8 | |
11. | Martin Gugat, Michael Herty, Axel Klar, Günther Leugering, Veronika Schleper, 2012, Chapter 7, 978-3-0348-0132-4, 123, 10.1007/978-3-0348-0133-1_7 | |
12. | Mapundi K. Banda, Michael Herty, Jean-Medard T. Ngnotchouye, Toward a Mathematical Analysis for Drift-Flux Multiphase Flow Models in Networks, 2010, 31, 1064-8275, 4633, 10.1137/080722138 | |
13. | Jeroen J. Stolwijk, Volker Mehrmann, Error Analysis and Model Adaptivity for Flows in Gas Networks, 2018, 26, 1844-0835, 231, 10.2478/auom-2018-0027 | |
14. | Mapundi K. Banda, Axel-Stefan Häck, Michael Herty, Numerical Discretization of Coupling Conditions by High-Order Schemes, 2016, 69, 0885-7474, 122, 10.1007/s10915-016-0185-x | |
15. | Evgenii S. Baranovskii, Vyacheslav V. Provotorov, Mikhail A. Artemov, Alexey P. Zhabko, Non-Isothermal Creeping Flows in a Pipeline Network: Existence Results, 2021, 13, 2073-8994, 1300, 10.3390/sym13071300 | |
16. | Rinaldo M. Colombo, Mauro Garavello, On the Cauchy Problem for the p-System at a Junction, 2008, 39, 0036-1410, 1456, 10.1137/060665841 | |
17. | J.B. Collins, P.A. Gremaud, Analysis of a domain decomposition method for linear transport problems on networks, 2016, 109, 01689274, 61, 10.1016/j.apnum.2016.06.004 | |
18. | Alfredo Bermúdez, Xián López, M. Elena Vázquez-Cendón, Treating network junctions in finite volume solution of transient gas flow models, 2017, 344, 00219991, 187, 10.1016/j.jcp.2017.04.066 | |
19. | Martin Gugat, Michael Herty, Siegfried Müller, Coupling conditions for the transition from supersonic to subsonic fluid states, 2017, 12, 1556-181X, 371, 10.3934/nhm.2017016 | |
20. | H. Egger, A Robust Conservative Mixed Finite Element Method for Isentropic Compressible Flow on Pipe Networks, 2018, 40, 1064-8275, A108, 10.1137/16M1094373 | |
21. | Yannick Holle, Kinetic relaxation to entropy based coupling conditions for isentropic flow on networks, 2020, 269, 00220396, 1192, 10.1016/j.jde.2020.01.005 | |
22. | Mohamed Elshobaki, Alessandro Valiani, Valerio Caleffi, Numerical modelling of open channel junctions using the Riemann problem approach, 2019, 57, 0022-1686, 662, 10.1080/00221686.2018.1534283 | |
23. | Mapundi K. Banda, Michael Herty, Towards a space mapping approach to dynamic compressor optimization of gas networks, 2011, 32, 01432087, 253, 10.1002/oca.929 | |
24. | Rinaldo M. Colombo, 2011, Chapter 13, 978-1-4419-9553-7, 267, 10.1007/978-1-4419-9554-4_13 | |
25. | Seok Woo Hong, Chongam Kim, A new finite volume method on junction coupling and boundary treatment for flow network system analyses, 2011, 65, 02712091, 707, 10.1002/fld.2212 | |
26. | Michael Herty, Mohammed Seaïd, Assessment of coupling conditions in water way intersections, 2013, 71, 02712091, 1438, 10.1002/fld.3719 | |
27. | Gunhild A. Reigstad, Existence and Uniqueness of Solutions to the Generalized Riemann Problem for Isentropic Flow, 2015, 75, 0036-1399, 679, 10.1137/140962759 | |
28. | R. Borsche, A. Klar, Flooding in urban drainage systems: coupling hyperbolic conservation laws for sewer systems and surface flow, 2014, 76, 02712091, 789, 10.1002/fld.3957 | |
29. | Pascal Mindt, Jens Lang, Pia Domschke, Entropy-Preserving Coupling of Hierarchical Gas Models, 2019, 51, 0036-1410, 4754, 10.1137/19M1240034 | |
30. | Alexandre Morin, Gunhild A. Reigstad, Pipe Networks: Coupling Constants in a Junction for the Isentropic Euler Equations, 2015, 64, 18766102, 140, 10.1016/j.egypro.2015.01.017 | |
31. | Mapundi Kondwani Banda, 2015, Chapter 9, 978-3-319-11321-0, 439, 10.1007/978-3-319-11322-7_9 | |
32. | Yogiraj Mantri, Sebastian Noelle, Well-balanced discontinuous Galerkin scheme for 2 × 2 hyperbolic balance law, 2021, 429, 00219991, 110011, 10.1016/j.jcp.2020.110011 | |
33. | Mauro Garavello, Benedetto Piccoli, Conservation laws on complex networks, 2009, 26, 0294-1449, 1925, 10.1016/j.anihpc.2009.04.001 | |
34. | Mauro Garavello, 2011, Chapter 15, 978-1-4419-9553-7, 293, 10.1007/978-1-4419-9554-4_15 | |
35. | Andrea Corli, Ingenuin Gasser, Mária Lukáčová-Medvid’ová, Arne Roggensack, Ulf Teschke, A multiscale approach to liquid flows in pipes I: The single pipe, 2012, 219, 00963003, 856, 10.1016/j.amc.2012.06.054 | |
36. | Raul Borsche, Jochen Kall, ADER schemes and high order coupling on networks of hyperbolic conservation laws, 2014, 273, 00219991, 658, 10.1016/j.jcp.2014.05.042 | |
37. | Mapundi K. Banda, Michael Herty, Multiscale modeling for gas flow in pipe networks, 2008, 31, 01704214, 915, 10.1002/mma.948 | |
38. | Gunhild A. Reigstad, Tore Flåtten, Nils Erland Haugen, Tor Ytrehus, Coupling constants and the generalized Riemann problem for isothermal junction flow, 2015, 12, 0219-8916, 37, 10.1142/S0219891615500022 | |
39. | Alfredo Bermúdez, Xián López, M. Elena Vázquez-Cendón, Finite volume methods for multi-component Euler equations with source terms, 2017, 156, 00457930, 113, 10.1016/j.compfluid.2017.07.004 | |
40. | Raul Borsche, Numerical schemes for networks of hyperbolic conservation laws, 2016, 108, 01689274, 157, 10.1016/j.apnum.2016.01.006 | |
41. | Alexandre Bayen, Maria Laura Delle Monache, Mauro Garavello, Paola Goatin, Benedetto Piccoli, 2022, Chapter 3, 978-3-030-93014-1, 39, 10.1007/978-3-030-93015-8_3 | |
42. | Christian Contarino, Eleuterio F. Toro, Gino I. Montecinos, Raul Borsche, Jochen Kall, Junction-Generalized Riemann Problem for stiff hyperbolic balance laws in networks: An implicit solver and ADER schemes, 2016, 315, 00219991, 409, 10.1016/j.jcp.2016.03.049 | |
43. | Michael Herty, Nouh Izem, Mohammed Seaid, Fast and accurate simulations of shallow water equations in large networks, 2019, 78, 08981221, 2107, 10.1016/j.camwa.2019.03.049 | |
44. | F. Daude, P. Galon, A Finite-Volume approach for compressible single- and two-phase flows in flexible pipelines with fluid-structure interaction, 2018, 362, 00219991, 375, 10.1016/j.jcp.2018.01.055 | |
45. | Benedetto Piccoli, Andrea Tosin, 2013, Chapter 576-3, 978-3-642-27737-5, 1, 10.1007/978-3-642-27737-5_576-3 | |
46. | Gunhild Allard Reigstad, Tore Flåtten, 2015, Chapter 66, 978-3-319-10704-2, 667, 10.1007/978-3-319-10705-9_66 | |
47. | F. Daude, R.A. Berry, P. Galon, A Finite-Volume method for compressible non-equilibrium two-phase flows in networks of elastic pipelines using the Baer–Nunziato model, 2019, 354, 00457825, 820, 10.1016/j.cma.2019.06.010 | |
48. | Benedetto Piccoli, Andrea Tosin, 2012, Chapter 112, 978-1-4614-1805-4, 1748, 10.1007/978-1-4614-1806-1_112 | |
49. | Mouhamadou Samsidy Goudiaby, Gunilla Kreiss, Existence result for the coupling of shallow water and Borda–Carnot equations with Riemann data, 2020, 17, 0219-8916, 185, 10.1142/S021989162050006X | |
50. | Michael Herty, Mohammed Seaïd, Simulation of transient gas flow at pipe-to-pipe intersections, 2008, 56, 02712091, 485, 10.1002/fld.1531 | |
51. | RINALDO M. COLOMBO, CRISTINA MAURI, EULER SYSTEM FOR COMPRESSIBLE FLUIDS AT A JUNCTION, 2008, 05, 0219-8916, 547, 10.1142/S0219891608001593 | |
52. | Mapundi K. Banda, Michael Herty, Jean Medard T. Ngnotchouye, On linearized coupling conditions for a class of isentropic multiphase drift-flux models at pipe-to-pipe intersections, 2015, 276, 03770427, 81, 10.1016/j.cam.2014.08.021 | |
53. | Christophe Chalons, Pierre-Arnaud Raviart, Nicolas Seguin, The interface coupling of the gas dynamics equations, 2008, 66, 0033-569X, 659, 10.1090/S0033-569X-08-01087-X | |
54. | Sara Grundel, Michael Herty, Hyperbolic discretization of simplified Euler equation via Riemann invariants, 2022, 106, 0307904X, 60, 10.1016/j.apm.2022.01.006 | |
55. | Zlatinka Dimitrova, Flows of Substances in Networks and Network Channels: Selected Results and Applications, 2022, 24, 1099-4300, 1485, 10.3390/e24101485 | |
56. | Edwige Godlewski, Pierre-Arnaud Raviart, 2021, Chapter 7, 978-1-0716-1342-9, 627, 10.1007/978-1-0716-1344-3_7 | |
57. | Jens Brouwer, Ingenuin Gasser, Michael Herty, Gas Pipeline Models Revisited: Model Hierarchies, Nonisothermal Models, and Simulations of Networks, 2011, 9, 1540-3459, 601, 10.1137/100813580 | |
58. | Raul Borsche, Jochen Kall, High order numerical methods for networks of hyperbolic conservation laws coupled with ODEs and lumped parameter models, 2016, 327, 00219991, 678, 10.1016/j.jcp.2016.10.003 | |
59. | MOUHAMADOU SAMSIDY GOUDIABY, GUNILLA KREISS, A RIEMANN PROBLEM AT A JUNCTION OF OPEN CANALS, 2013, 10, 0219-8916, 431, 10.1142/S021989161350015X | |
60. | Martin Gugat, Michael Herty, 2022, 23, 9780323850599, 59, 10.1016/bs.hna.2021.12.002 | |
61. | Benedetto Piccoli, Andrea Tosin, 2009, Chapter 576, 978-0-387-75888-6, 9727, 10.1007/978-0-387-30440-3_576 | |
62. | Gunhild A. Reigstad, Numerical network models and entropy principles for isothermal junction flow, 2014, 9, 1556-181X, 65, 10.3934/nhm.2014.9.65 | |
63. | Andrea Corli, Massimiliano D. Rosini, Ulrich Razafison, 2024, Mathematical Modeling of Chattering and the Optimal Design of a Valve*, 979-8-3503-1633-9, 76, 10.1109/CDC56724.2024.10886245 | |
64. | Michael T. Redle, Michael Herty, An asymptotic-preserving scheme for isentropic flow in pipe networks, 2025, 20, 1556-1801, 254, 10.3934/nhm.2025013 | |
65. | Andrea Corli, Ulrich Razafison, Massimiliano D. Rosini, Coherence of Coupling Conditions for the Isothermal Euler System, 2025, 0170-4214, 10.1002/mma.10847 |