Dr. Sebastian Ullmann

 

Research topic

Uncertainty Quantification

 

Numerical MathematicsDepartment of Mathematics

Address:

Dolivostr. 15

D-64293 Darmstadt

Germany

Phone:

+49 6151 16 - 24396

Fax:

+49 6151 16 - 24404

Office:

S4|10 229

Email:

ullmann (at) gsc.tu...

Research interests

  • Numerical methods for PDEs with random data
  • Model order reduction for parametrized PDEs
  • Adaptive finite element methods
  • Computational fluid dynamics

Research group

Teaching

Summer 2017

Reduced Basis Methods

lecturer

Summer 2016

Stochastic Finite Elements

lecturer

Summer 2015

Practical Studies in Computational Engineering

lecturer

Winter 2013/14

Numerical Analysis of Evolution Equations

assistant

Winter 2013/14

Introduction to Numerical Analysis

assistant

Summer 2010

Numerik Parabolischer Differentialgleichungen

assistant

Summer 2009

Numerische Mathematik für Maschinenbau

assistant

Winter 2008/09

Mathematik III f. MB/MPE, LaB/WFM, VI, WI(MB)

assistant

Publications

Gräßle, Carmen ; Hinze, Michael ; Lang, Jens ; Ullmann, Sebastian :
POD model order reduction with space-adapted snapshots for incompressible flows.
[Online-Edition: https://arxiv.org/abs/1810.03892]
In: Advances in Computational Mathematics ISSN 1019-7168
[Article] , (2018) (Eingereicht)
Müller, Christopher ; Ullmann, Sebastian ; Lang, Jens
Schäfer, Michael ; Behr, Marek ; Mehl, Miriam ; Wohlmuth, Barbara (eds.) :

A Bramble-Pasciak conjugate gradient method for discrete Stokes problems with lognormal random viscosity.
[Online-Edition: https://link.springer.com/chapter/10.1007/978-3-319-93891-2_...]
In: Recent Advances in Computational Engineering. Lecture Notes in Computational Science and Engineering, 124. Springer International Publishin, Cham , pp. 63-87. ISBN 978-3-319-93891-2
[Book Section] , (2018)
Spannring, Christopher ; Ullmann, Sebastian ; Lang, Jens
Schäfer, Michael ; Behr, Marek ; Mehl, Miriam ; Wohlmuth, Barbara (eds.) :

A weighted reduced basis method for parabolic PDEs with random data.
[Online-Edition: https://link.springer.com/chapter/10.1007/978-3-319-93891-2_...]
In: Recent Advances in Computational Engineering. Lecture Notes in Computational Science and Engineering, 124. Springer International Publishing, Cham , pp. 145-161. ISBN 978-3-319-93891-2
[Book Section] , (2018)
Müller, Christopher ; Ullmann, Sebastian ; Lang, Jens :
A Bramble-Pasciak conjugate gradient method for discrete Stokes equations with random viscosity.
[Online-Edition: https://arxiv.org/abs/1801.01838]
In: SIAM/ASA Journal on Uncertainty Quantification (JUQ)
[Article] , (2018) (Eingereicht)
Ullmann, Sebastian ; Rotkvic, Marko ; Lang, Jens :
POD-Galerkin reduced-order modeling with adaptive finite element snapshots.
[Online-Edition: http://dx.doi.org/10.1016/j.jcp.2016.08.018]
In: Journal of Computational Physics, 325 pp. 244-258. ISSN 0021-9991
[Article] , (2016)
Ullmann, Sebastian :
POD-galerkin modeling for incompressible flows with stochastic boundary conditions.
[Online-Edition: http://www.dr.hut-verlag.de/9783843915687.html]
Dr. Hut , München
[Ph.D. Thesis], (2014)
Ullmann, Sebastian ; Lang, Jens
Garcke, J. ; Pflüger, D. (eds.) :

POD-Galerkin Modeling and Sparse-Grid Collocation for a Natural Convection Problem with Stochastic Boundary Conditions.
[Online-Edition: http://link.springer.com/chapter/10.1007%2F978-3-319-04537-5...]
In: Sparse Grids and Applications - Munich 2012. Lecture Notes in Computational Science and Engineering. Springer , pp. 295-315.
[Book Section] , (2014)
Ullmann, Sebastian ; Löbig, Stefan ; Lang, Jens
Janicka, Johannes ; Sadiki, Amsini ; Schäfer, Michael ; Heeger, Christof (eds.) :

Adaptive Large Eddy Simulation and Reduced-Order Modeling.
[Online-Edition: https://doi.org/10.1007/978-94-007-5320-4_12]
In: Flow and Combustion in Advanced Gas Turbine Combustors. Fluid Mechanics and Its Applications, 102. Springer Netherlands, Dordrecht Dordrecht , pp. 349-378. ISBN 978-94-007-5320-4
[Book Section] , (2013)
Ullmann, Sebastian ; Lang, Jens :
POD and CVT Galerkin reduced-order modeling of the flow around a cylinder.
[Online-Edition: http://onlinelibrary.wiley.com/doi/10.1002/pamm.201210337/pd...]
In: Proceedings in Applied Mathematics and Mechanics (PAMM), 12 (1) pp. 697-698. ISSN 1617-7061
[Article] , (2012)
Ullmann, Sebastian ; Lang, Jens
Pereira, J. C. F. ; Sequeira, A. ; Pereira, J. M. C. (eds.) :

A POD-Galerkin Reduced Model with Updated Coefficients for Smagorinsky LES.
[Online-Edition: http://www.mathematik.tu-darmstadt.de/preprint.php?id=2610]
In: Proceedings of the V European Conference on Computational Fluid Dynamics ECCOMAS CFD 2010
[Article] , (2010)

Talks

  • 29.05.2018: Stochastic Galerkin reduced basis methods for parametrized random elliptic PDEs
    Uncertainty quantification for complex systems: theory and methodologies, Isaac Newton Institute, Cambridge, UK
  • 28.09.2017: Model order reduction and spatial adaptivity for incompressible flows with random data
    ICCE, Darmstadt
  • 08.09.2017: CFD under uncertainty: combining model order reduction with spatial adaptivity
    FrontUQ, München
  • 01.03.2017: Adaptive reduced-order modeling for flows under uncertainty
    SIAM CSE, Atlanta
  • 02.06.2016: POD-Galerkin reduced-order modeling with adaptive finite element snapshots
    Numerik Kolloquium, Ulm
  • 07.04.2016: POD-Galerkin modeling with adaptive finite elements for stochastic sampling
    SIAM UQ 2016, Lausanne
  • 07.12.2015: Model order reduction with adaptive finite element POD and application to uncertainty quantification
    Seminar in Numerical Analysis, Universität Basel
  • 12.11.2015: Adaptive finite element POD for uncertainty quantification
    Direct and Inverse Problems for PDEs with Random Coefficients, WIAS Berlin
  • 15.10.2015: POD-Galerkin for finite elements with dynamic mesh adaptivity
    MoRePaS III, Triest
  • 26.05.2015: Natural convection with random boundary conditions: A comparison of techniques
    UNCECOMP 2015, Crete
  • 13.04.2015: Reduced-order modeling for UQ
    5th retreat of the Graduate School CE, Seeheim-Jugenheim
  • 27.02.2015: Research in uncertainty quantification
    Workshop Numerical Analysis and Scientific Computing, Hirschegg
  • 12.11.2014: What is ... a finite element fictitious boundary method?
    Einführungsvortrag zum Mathematischen Kolloquium, TU Darmstadt
  • 08.08.2014: POD-Galerkin reduced-order modeling and stochastic collocation for natural convection under uncertainty
    Advances in Simulation-Driven Optimization and Modeling (ASDOM 2014), Reykjavik
  • 26.05.2014: POD-Galerkin modeling for a steady thermally driven flow in a cavity with stochastic boundary conditions
    Workshop on Uncertainty Quantification in Computational Fluid Dynamics, Pisa
  • 24.01.2013: POD-Galerkin reduzierte Modelle für Strömungsprobleme mit stochastischen Randbedigungungen
    Seminar Numerische Mathematik, WIAS Berlin
  • 29.11.2012: POD-Galerkin-Modellierung thermo-konvektiver Strömungen
    MetStröm Bündeltreffen, Darmstadt
  • 27.03.2012: POD and CVT Galerkin reduced modeling of the flow around a cylinder
    GAMM 2012, Darmstadt
  • 16.06.2010: A POD-Galerkin reduced model with updated coefficients for Smagorinsky LES
    ECCOMAS CFD 2010, Lissabon
  • 16.12.2009: A POD reduced model for Smagorinsky large-eddy simulation
    Mathematical Physics Seminar, TU Delft
  • 29.10.2009: Adaptive error control in LES: reduced POD-Galerkin modelling
    SFB 568 Klausurtagung, Seeheim-Jugenheim

Posters

  • 16.04.2018: Stochastic Galerkin reduced basis methods for parametrized random elliptic PDEs
    SIAM UQ, Garden Grove (USA)
  • 12.04.2018: Stochastic Galerkin reduced basis methods for parametrized random elliptic PDEs
    MoRePaS Conference, Nantes
  • 05.03.2018: Stochastic Galerkin reduced basis methods for parametrized random elliptic PDEs
    UNQW03 Workshop, Isaac Newton Institute, Cambridge (UK)
  • 18.11.2016: POD-Galerkin reduced-order modeling with adaptive finite element snapshots
    KoMSO Challenge Workshop, Renningen
  • 16.03.2015: Uncertainty quantification for thermally driven flow
    SIAM CSE, Salt Lake City
  • 10.09.2015: Space-adaptive POD for a Burgers problem with stochastic data
    2nd GAMM AGUQ Workshop on Uncertainty Quantification, Chemnitz
  • 09.09.2014: POD aided stochastic collocation for natural convection under uncertainty
    NASPDE, Lausanne
  • 04.10.2012: POD-Galerkin modeling of thermo-convective Poiseuille flow
    MoRePaS II, Günzburg
  • 17.11.2011: POD and CVT Galerkin reduced modeling of the laminar vortex-shedding flow around a cylinder
    Workshop RB, POD and PGD, Cachan

Contact

Technische Universität Darmstadt

Graduate School CE
Dolivostraße 15
D-64293 Darmstadt

Phone+49 6151/16-24401    or
-24402
Fax+49 6151/16-24404
OfficeS4|10-322

to assistants' office

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