Kristian Debrabant

2024

B-series for SDEs with application to exponential integrators for non-autonomous semi-linear problems

Arara, A. A., Debrabant, K. & Kværnø, A., okt. 2024, I: Journal of Computational Dynamics. 11, 4, s. 533-546 14 s.

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Exponential Euler method for stiff stochastic differential equations with additive fractional Brownian noise

Kamrani, M., Debrabant, K. & Jamshidi, N., 2024, I: International Journal of Computer Mathematics. 101, 3, s. 357-371

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2023

Weak convergence of balanced stochastic Runge-Kutta methods for stochastic differential equations

Rathinasamy, A., Debrabant, K. & Nair, P., 2023, I: Research in Mathematics. 10, 1, 2163546.

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2022

A Comparison of Different Approaches to Dynamic Origin-Destination Matrix Estimation in Urban Traffic

Andersen, N. S., Chiarandini, M. & Debrabant, K., 31. maj 2022, arXiv.

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Hepatitis C time trends in reported cases and estimates of the hidden population born before 1965, Denmark and Sweden, 1990 to 2020

Christensen, P. B., Debrabant, B., Cowan, S., Debrabant, K., Øvrehus, A. L. H. & Duberg, A.-S., 15. dec. 2022, I: Eurosurveillance. 27, 50, 9 s., 2200243.

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Lawson schemes for highly oscillatory stochastic differential equations and conservation of invariants

Debrabant, K., Kværnø, A. & Mattsson, N. C., 2022, I: BIT Numerical Mathematics. 62, s. 1121–1147

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2021

Backward Differentiation Formula finite difference schemes for diffusion equations with an obstacle term

Bokanowski, O. & Debrabant, K., apr. 2021, I: IMA Journal of Numerical Analysis. 41, 2, s. 900-934

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En uoverskuelig pris

Debrabant, K., Grønbæk, L. & Kronborg, C., 10. feb. 2021, I: Weekendavisen.

Publikation: Bidrag til tidsskrift › Bidrag til avis - Avisartikel

Runge–Kutta Lawson schemes for stochastic differential equations

Debrabant, K., Kværnø, A. & Mattsson, N. C., 2021, I: BIT Numerical Mathematics. 61, 2, s. 381-409

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The Cost-Effectiveness of a COVID-19 Vaccine in a Danish Context

Debrabant, K., Grønbæk, L. & Kronborg, C., nov. 2021, I: Clinical Drug Investigation. 41, 11, s. 975-988

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The cost-effectiveness of a COVID-19 vaccine in a Danish context

Debrabant, K., Grønbæk, L. & Kronborg, C., 31. jan. 2021, Syddansk Universitet, (Discussion Papers on Business and Economics; Nr. 2, Bind 2021).

Publikation: Working paper › Forskning

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2020

High order numerical integrators for single integrand Stratonovich SDEs

Cohen, D., Debrabant, K. & Rößler, A., dec. 2020, I: Applied Numerical Mathematics. 158, s. 264-270

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Study of micro-macro acceleration schemes for linear slow-fast stochastic differential equations with additive noise

Debrabant, K., Samaey, G. & Zieliński, P., 2020, I: BIT Numerical Mathematics. 60, s. 959–998

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2019

Analysis of multilevel Monte Carlo path simulation using the Milstein discretisation

Giles, M., Debrabant, K. & Rößler, A., 2019, I: Discrete and Continuous Dynamical Systems - Series B. 24, 8, s. 3881-3903

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Parametric model reduction via interpolating orthonormal bases

Zimmermann, R. & Debrabant, K., 5. jan. 2019, Numerical Mathematics and Advanced Applications: ENUMATH 2017. Radu, F. A., Kumar, K., Berre, I., Nordbotten, J. M. & Pop, I. S. (red.). Springer, s. 683-691 (Lecture Notes in Computational Science and Engineering, Bind 126).

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Stochastic B-series and order conditions for exponential integrators

Arara, A. A., Debrabant, K. & Kværnø, A., 5. jan. 2019, Numerical Mathematics and Advanced Applications: ENUMATH 2017. Radu, F. A., Kumar, K., Berre, I., Nordbotten, J. M. & Pop, I. S. (red.). Springer, s. 419-427 (Lecture Notes in Computational Science and Engineering, Bind 126).

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Weak antithetic MLMC estimation of SDEs with the Milstein scheme for low-dimensional Wiener processes

Debrabant, K., Ghasemifard, A. & Mattsson, N. C., maj 2019, I: Applied Mathematics Letters. 91, s. 22-27 6 s.

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2018

Carbon oxidation and bioirrigation in sediments along a Skagerrak−Kattegat−Belt Sea depth transect

Kristensen, E., Røy, H., Debrabant, K. & Valdemarsen, T., okt. 2018, I: Marine Ecology Progress Series. 604, s. 33-50

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General order conditions for stochastic partitioned Runge-Kutta methods

Anmarkrud, S., Debrabant, K. & Kværnø, A., 2018, I: BIT Numerical Mathematics. 58, 2, s. 257–280

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Robust optimization with applications to design of context specific robot solutions

Jørgensen, T. B., Wolniakowski, A., Petersen, H. G., Debrabant, K. & Krüger, N., 2018, I: Robotics and Computer-Integrated Manufacturing. 53, s. 162–177

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2017

A micro-macro acceleration method for the Monte Carlo simulation of stochastic differential equations

Debrabant, K., Samaey, G. & Zieliński, P., 2017, I: SIAM Journal on Numerical Analysis. 55, 6, s. 2745-2786

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Cheap arbitrary high order methods for single integrand SDEs

Debrabant, K. & Kværnø, A., 2017, I: BIT Numerical Mathematics. 57, 1, s. 153-168

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2016

Robust optimization of robotic pick and place operations for deformable objects through simulation

Bo Jorgensen, T., Debrabant, K. & Kruger, N., 2016, Proceeding of the IEEE International Conference on Robotics and Automation. IEEE Press, s. 3863-3870

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

2015

On Asymptotic Global Error Estimation and Control of Finite Difference Solutions for Semilinear Parabolic Equations

Debrabant, K. & Lang, J., 2015, I: Computer Methods in Applied Mechanics and Engineering. 288, s. 110-126

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On the acceleration of the multi-level Monte Carlo method

Debrabant, K. & Rößler, A., 1. jun. 2015, I: Journal of Applied Probability. 52, 2, s. 307-322

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On the global error of special Runge–Kutta methods applied to linear Differential Algebraic Equations

Debrabant, K., González-Pinto, S. & Hernández-Abreu, D., jan. 2015, I: Applied Mathematics Letters. 39, s. 53-59

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2014

Semi-Lagrangian schemes for linear and fully non-linear Hamilton-Jacobi-Bellman equations

Debrabant, K. & Jakobsen, E. R., 2014, Hyperbolic Problems: Theory, Numerics, Applications: Proceedings of the Fourteenth International Conference on Hyperbolic Problems held in Padova, June 25-29, 2012. Ancona, F., Bressan, A., Marcati, P. & Marson, A. (red.). Springfield, MO: American Institute of Mathematical Sciences (AIMS), s. 483-490 (Applied Mathematics, Bind 8).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

2013

Derivative-free weak approximation methods for stochastic differential equations in finance

Debrabant, K. & Rößler, A., 2013, Recent Developments in Computational Finance: Foundations, Algorithms and Applications. Gerstner, T. & Kloeden, P. (red.). World Scientific, s. 299-316 (Interdisciplinary Mathematical Sciences, Bind 14).

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On global error estimation and control of finite difference solutions for parabolic equations

Debrabant, K., 2013, Adaptive Modeling and Simulation 2013. Moitinho de Almeida, J. P., Díez, P., Tiago, C. & Parés, N. (red.). Barcelona, Spain: International Center for Numerical Methods in Engineering, s. 187-198

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Semi-Lagrangian schemes for linear and fully non-linear diffusion equations

Debrabant, K. & Jakobsen, E. R., 2013, I: Mathematics of Computation. 82, 283, s. 1433-1462

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Semi-Lagrangian schemes for parabolic equations

Debrabant, K. & Jakobsen, E. R., 2013, Recent developments in computational finance: Foundations, Algorithms and Applications. Gerstner, T. & Kloeden, P. (red.). World Scientific, s. 279-298 (Interdisciplinary Mathematical Sciences, Bind 14).

Publikation: Kapitel i bog/rapport/konference-proceeding › Kapitel i bog › Forskning › peer review

2011

B-series analysis of iterated Taylor methods

Debrabant, K. & Kværnø, A., 2011, I: BIT Numerical Mathematics. 51, 3, s. 529-553 25 s.

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Composition of stochastic B-series with applications to implicit Taylor methods

Debrabant, K. & Kværnø, A., 2011, I: Applied Numerical Mathematics. 61, 4, s. 501-511 11 s.

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2010

Consistency analysis of stochastic Runge-Kutta and Taylor methods

Debrabant, K., 2010, Darmstadt.

Publikation: Afhandling › Doktorafhandling

Runge-Kutta methods for third order weak approximation of SDEs with multidimensional additive noise

Debrabant, K., 2010, I: BIT Numerical Mathematics. 50, 3, s. 541-558 18 s.

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Stochastic Taylor expansions: Weight functions of B-series expressed as multiple integrals

Debrabant, K. & Kværnø, A., 2010, I: Stochastic Analysis and Applications. 28, 2, s. 293 - 302

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2009

Diagonally drift-implicit Runge-Kutta methods of weak order one and two for Itô SDEs and stability analysis

Debrabant, K. & Rößler, A., 2009, I: Applied Numerical Mathematics. 59, 3-4, s. 595-607 13 s.

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Families of efficient second order Runge-Kutta methods for the weak approximation of Itô stochastic differential equations

Debrabant, K. & Rößler, A., 2009, I: Applied Numerical Mathematics. 59, 3-4, s. 582-594 13 s.

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2008

2007

On global error control for parabolic PDEs

Lang, J., Debrabant, K. & Verwer, J., 2007, I: Oberwolfach Reports. 4, s. 1702-1704 3 s.

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2005

Convergence of Runge-Kutta methods applied to linear partial differential-algebraic equations

Debrabant, K. & Strehmel, K., 2005, I: Applied Numerical Mathematics. 53, 2-4, s. 213-229 17 s.

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2004

Numerische Behandlung linearer und semilinearer partieller differentiell-algebraischer Systeme mit Runge-Kutta-Methoden

Debrabant, K., okt. 2004

Publikation: Afhandling › Doktorafhandling

Statik eines Flachdaches

Debrabant, K. & Kiehl, M., 2004, Mathematische Modellierung mit Schülern.

Publikation: Kapitel i bog/rapport/konference-proceeding › Bidrag til kompendium/lecture notes

2002

On quasi-linear PDAEs with convection: applications, indices, numerical solution

Lucht, W. & Debrabant, K., 2002, I: Applied Numerical Mathematics. 42, 1-3, s. 297-314 18 s.

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Kristian Debrabant

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