Meno a priezvisko:
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Dr. rer. nat. Tatiana Kossaczká, MSc.
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Typ dokumentu:
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Vedecko/umelecko-pedagogická charakteristika osoby
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Názov vysokej školy:
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Univerzita Komenského v Bratislave
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Sídlo vysokej školy:
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Šafárikovo námestie 6, 818 06 Bratislava
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III.a - Zamestnanie-pracovné zaradenie | III.b - Inštitúcia | III.c - Časové vymedzenie |
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odborná asistentka | Bergische Universität Wuppertal, Fakulta matematiky a prírodných vied | 2017-2018 |
vedecká asistentka | Bergische Universität Wuppertal, Fakulta matematiky a prírodných vied | 2020-2024 |
IV.a - Popis aktivity, názov kurzu (ak išlo o kurz), iné | IV.b - Názov inštitúcie | IV.c - Rok |
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Úvod do numerickej matematiky | Bergische Universität Wuppertal, Fakulta matematiky a prírodných vied | 2018 |
Numerické metódy na riešenie obyčajných diferenciálnych rovníc | Bergische Universität Wuppertal, Fakulta matematiky a prírodných vied / | 2020 |
Pokročilá matematika | Bergische Universität Wuppertal, Fakulta matematiky a prírodných vied / | 2023 |
Programovací seminár: Úvod do numerickej matematiky | Bergische Universität Wuppertal, Fakulta matematiky a prírodných vied / | 2024 |
V.1.a - Názov profilového predmetu | V.1.b - Študijný program | V.1.c - Stupeň | V.1.d - Študijný odbor |
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Pravdepodobnosť a štatistika pre informatikov | Aplikovaná informatika | I. | Informatika |
Numerické modelovanie | Ekonomicko-finančná matematika a modelovanie | II. | Matematika |
V.5.a - Názov predmetu | V.5.b - Študijný program | V.5.c - Stupeň | V.5.d - Študijný odbor |
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Finančné deriváty | Ekonomicko-finančná matematika a modelovanie | II. | Matematika |
Kossaczká, T., Ehrhardt, M., & Günther, M.: Enhanced fifth order WENO shock-capturing schemes with deep learning. Results in Applied Mathematics, 12, 100201. 2021.
Kossaczká, T., Ehrhardt, M., & Günther, M.: A neural network enhanced weighted essentially non-oscillatory method for nonlinear degenerate parabolic equations. Physics of Fluids, 34(2), 026604. 2022.
Kossaczká, T., Ehrhardt, M., & Günther, M.: A deep smoothness WENO method with applications in option pricing. European Consortium for Mathematics in Industry. Springer International Publishing, 417-423. 2022.
Kossaczká, T., Ehrhardt, M., & Günther, M.: Deep FDM: Enhanced finite difference methods by deep learning. Franklin Open, 4, 100039. 2023.
Kossaczká, T., Jagtap, A.D. & Ehrhardt, M.: Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators. Physics of Fluids, 36(3), 036603. 2024.
Kossaczká, T., Ehrhardt, M., & Günther, M.: Enhanced fifth order WENO shock-capturing schemes with deep learning. Results in Applied Mathematics, 12, 100201. 2021.
Kossaczká, T., Ehrhardt, M., & Günther, M.: A neural network enhanced weighted essentially non-oscillatory method for nonlinear degenerate parabolic equations. Physics of Fluids, 34(2), 026604. 2022.
Kossaczká, T., Ehrhardt, M., & Günther, M.: A deep smoothness WENO method with applications in option pricing. European Consortium for Mathematics in Industry. Springer International Publishing, 417-423. 2022.
Kossaczká, T., Ehrhardt, M., & Günther, M.: Deep FDM: Enhanced finite difference methods by deep learning. Franklin Open, 4, 100039. 2023.
Kossaczká, T., Jagtap, A.D. & Ehrhardt, M.: Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators. Physics of Fluids, 36(3), 036603. 2024.
Zeifang, J. and Beck, A., 2021. A data-driven high order sub-cell artificial viscosity for the discontinuous Galerkin spectral element method. Journal of Computational Physics, 441, p.110475.
Ning, J., Su, X. and Xu, X., 2022. Improved fifth-order weighted essentially non-oscillatory scheme with low dissipation and high resolution for compressible flows. Physics of Fluids, 34(5).
Zhang, X., Huang, L., Jiang, Z. and Yan, C., 2022. A class of high-order improved fast weighted essentially non-oscillatory schemes for achieving optimal order at any critical points. Physics of Fluids, 34(12).
Wang, Z., Zhu, J., Wang, C. and Zhao, N., 2022. Finite difference alternative unequal-sized weighted essentially non-oscillatory schemes for hyperbolic conservation laws. Physics of Fluids, 34(11).
Drozda, L., Mohanamuraly, P., Cheng, L., Lapeyre, C., Daviller, G., Realpe, Y., Adler, A., Staffelbach, G. and Poinsot, T., 2023. Learning an optimised stable Taylor-Galerkin convection scheme based on a local spectral model for the numerical error dynamics. Journal of Computational Physics, 493, p.112430.
DAAD-MŠ ENANEFA – Efficient Numerical Approximation of Nonlinear Equations in Financial Applications