This image shows Alexander Schlaich

Alexander Schlaich

Dr.

Independent Junior Research Group Leader for Multiscale Materials Modeling

Contact

Universitätsstraße 32
70569 Stuttgart
Deutschland
Room: 227b

Office Hours

Consultation by appointment

Subject

  • Transport properties in porous media, hydrodynamic breakdown, continuum models at the nanoscale.
  • Electrostatic interactions in nano-confinement.
  • Charge transport in porous materials for improved electrode materials in energy storage.
  • Prediction of meso-/macroscale properties from molecular interactions.
  • Surface interactions, hydration forces, mechanical properties and surface chemistry.
  • Interactions between lipid bilayers and biological relevance of membrane composition.
  1. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Linear tracking MPC for nonlinear systems part II: the data-driven case,” IEEE Trans. Automat. Control, vol. 67, no. 9, Art. no. 9, 2022, doi: 10.1109/TAC.2022.3166851.
  2. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Linear tracking MPC for nonlinear systems part I: the model-based case,” IEEE Trans. Automat. Control, vol. 67, no. 9, Art. no. 9, 2022, doi: 10.1109/TAC.2022.3166872.
  3. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-driven model predictive control: closed-loop guarantees and experimental results,” at-Automatisierungstechnik, vol. 69, no. 7, Art. no. 7, 2021, doi: 10.1515/auto-2021-0024.
  4. J. Berberich, S. Wildhagen, M. Hertneck, and F. Allgöwer, “Data-driven analysis and control of continuous-time systems under aperiodic sampling,” in Proc. 19th IFAC Symp. System Identification (SYSID), Padova, Italy, 2021, pp. 210–215. doi: 10.1016/j.ifacol.2021.08.360.
  5. N. Wieler, J. Berberich, A. Koch, and F. Allgöwer, “Data-driven controller design via finite-horizon dissipativity,” in Proc. 3rd Learning for Dynamics and Control Conf. (L4DC), Zürich, Switzerland, 2021, vol. 144, pp. 287–298.
  6. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “On the design of terminal ingredients for data-driven MPC,” in Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC), Bratislava, Slovakia, 2021, pp. 257–263. doi: 10.1016/j.ifacol.2021.08.554.
  7. M. Alsalti, J. Berberich, V. G. Lopez, F. Allgöwer, and M. A. Müller, “Data-Based System Analysis and Control of Flat Nonlinear Systems,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 1484–1489. doi: 10.1109/CDC45484.2021.9683327.
  8. M. Köhler, J. Berberich, M. A. Müller, and F. Allgöwer, “Data-driven distributed MPC of dynamically coupled linear systems,” in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems (MTNS), Bayreuth, Germany, 2022, pp. 906–911.
  9. M. Ibach et al., “Numerical Investigation of Multiple Droplet Streams and the Effect on Grouping Behavior,” presented at the ILASS-Europe 2022, 31th Conference on Liquid Atomization and Spray Systems, 6-8 September 2022, Tel-Aviv (Virtual), 2022.
  10. A. Arad, V. Vaikuntanathan, M. Ibach, J. B. Greenberg, B. Weigand, and D. Katoshevski, “CFD Simulations of Droplet Grouping in Acoustic Standing Waves,” presented at the ILASS-Europe 2022, 31th Conference on Liquid Atomization and Spray Systems, 6-8 September 2022, Tel-Aviv (Virtual), 2022.
  11. A. Arad, V. Vaikuntanathan, M. Ibach, J. B. Greenberg, B. Weigand, and D. Katoshevski, “CFD Simulations of Droplet Grouping in Acoustic Standing Waves,” presented at the ILASS-Europe 2022, 31th Conference on Liquid Atomization and Spray Systems, 6-8 September 2022, Tel-Aviv (Virtual), 2022.
  12. M. Köhler, J. Berberich, M. A. Müller, and F. Allgöwer, “Data-driven distributed MPC of dynamically coupled linear systems,” in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems, Bayreuth, Germany, 2022, pp. 906–911.
  13. Molpeceres, G., Kästner, J., Herrero, V. J., Peláez, R. J., and Maté, B., “Desorption of organic molecules from interstellar ices, combining experiments and computer simulations: Acetaldehyde as a case study,” Astron. Astrophys., vol. 664, p. A169, 2022, doi: 10.1051/0004-6361/202243489.
  14. M. Zinßer et al., “Irradiation-dependent topology optimization of metallization grid patterns and variation of contact layer thickness used for latitude-based yield gain of thin-film solar modules,” MRS Advances, 2022, doi: 10.1557/s43580-022-00321-3.
  15. M. Ibach, V. Vaikuntanathan, A. Arad, D. Katoshevski, J. B. Greenberg, and B. Weigand, “Investigation of droplet grouping in monodisperse streams by direct numerical simulations,” Physics of Fluids, vol. 34, no. 8, Art. no. 8, 2022, doi: 10.1063/5.0097551.
  16. M. Hertneck and F. Allgöwer, “Dynamic self-triggered control for nonlinear systems with delays,” in Proc. 9th IFAC Conf. on Networked Systems (NECSYS), Zürich, Switzerland, 2022, pp. 312–317. doi: 10.1016/j.ifacol.2022.07.278.
  17. D. Holzmüller and I. Steinwart, “Training two-layer ReLU networks with gradient descent is inconsistent,” Journal of Machine Learning Research, vol. 23, no. 181, Art. no. 181, 2022, [Online]. Available: http://jmlr.org/papers/v23/20-830.html
  18. D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart, “A Framework and Benchmark for Deep Batch Active Learning for Regression,” arXiv:2203.09410, 2022.
  19. H. M. and F. Allgöwer, “Dynamic self-triggered control for nonlinear systems with delays,” in Proc. 9th IFAC Conf. on Networked Systems (NECSYS), Zürich, Switzerland, 2022, pp. 312–317. doi: 10.1016/j.ifacol.2022.07.278.
  20. A. Gonzalez-Nicolas et al., “Optimal Exposure Time in Gamma-Ray Attenuation Experiments for Monitoring Time-Dependent Densities,” Transport in Porous Media, vol. 143, no. 2, Art. no. 2, 2022, doi: 10.1007/s11242-022-01777-5.
  21. V. Vaikuntanathan et al., “An Analytical Study on the Mechanism of Grouping of Droplets,” Fluids, vol. 7, no. 5, Art. no. 5, 2022, doi: 10.3390/fluids7050172.
  22. Molpeceres, G. et al., “Hydrogen abstraction reactions in formic and thioformic acid isomers by hydrogen and deuterium atoms,” Astron. Astrophys., vol. 663, p. A41, 2022, doi: 10.1051/0004-6361/202243366.
  23. C. Brecher et al., “Commitment zu aktivem Daten- und -softwaremanagement in großen Forschungsverbünden,” Bausteine Forschungsdatenmanagement, vol. 1, 2022, doi: 10.17192/BFDM.2022.1.8412.
  24. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” IEEE Trans. Automat. Control (submitted), Preprint:  arXiv:2108.11298, 2022.
  25. T. Martin and F. Allgöwer, “Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures,” IEEE Trans. Automat. Control (submitted), Preprint: arXiv:2103.10306, 2021.
  26. C. Boccellato and M. Rehm, “Glioblastoma, from disease understanding towards optimal cell-based in vitro models,” Cellular Oncology, 2022, doi: 10.1007/s13402-022-00684-7.
  27. S. Adam et al., “Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns,” Communications Biology, vol. 5, no. 1, Art. no. 1, 2022, doi: 10.1038/s42003-022-03033-4.
  28. T. Martin and F. Allgöwer, “Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation,” in Proc. American Control Conf. (ACC), Atlanta, GA, USA, 2022, pp. 1432–1437.
  29. W. F. van Gunsteren, M. Pechlaner, L. J. Smith, B. Stankiewicz, and N. Hansen, “A Method to Derive Structural Information on Molecules from Residual Dipolar Coupling NMR Data,” The Journal of Physical Chemistry B, vol. 126, no. 21, Art. no. 21, 2022, doi: 10.1021/acs.jpcb.2c02410.
  30. D. Markthaler, M. Fleck, B. Stankiewicz, and N. Hansen, “Exploring the Effect of Enhanced Sampling on Protein Stability Prediction,” Journal of Chemical Theory and Computation, vol. 18, no. 4, Art. no. 4, 2022, doi: 10.1021/acs.jctc.1c01012.
  31. C. Kessler et al., “Influence of layer slipping on adsorption of light gases in covalent organic frameworks: A combined experimental and computational study,” Microporous and Mesoporous Materials, vol. 336, p. 111796, 2022, doi: 10.1016/j.micromeso.2022.111796.
  32. M. Pechlaner, W. F. van Gunsteren, N. Hansen, and L. J. Smith, “Molecular dynamics simulation or structure refinement of proteins: are solvent molecules required? A case study using hen lysozyme,” European Biophysics Journal, vol. 51, no. 3, Art. no. 3, 2022, doi: 10.1007/s00249-022-01593-1.
  33. D. Markthaler and N. Hansen, “Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B,” Data in Brief, vol. 39, p. 107618, 2021, doi: 10.1016/j.dib.2021.107618.
  34. D. Markthaler, H. Kraus, and N. Hansen, “Binding free energies for the SAMPL8 CB8 ‘Drugs of Abuse’ challenge from umbrella sampling combined with Hamiltonian replica exchange,” Journal of Computer-Aided Molecular Design, vol. 36, pp. 1–9, 2022, doi: 10.1007/s10822-021-00439-w.
  35. D. Markthaler, H. Kraus, and N. Hansen, “Binding free energies for the SAMPL8 CB8 ``Drugs of Abuse’’ challenge from umbrella sampling combined with Hamiltonian replica exchange,” Journal of Computer-Aided Molecular Design, vol. 36, no. 1, Art. no. 1, 2022, doi: 10.1007/s10822-021-00439-w.
  36. B. Hillebrecht and B. Unger, “Certified machine learning: A posteriori error estimation for physics-informed neural networks,” ArXiv e-print 2203.17055, 2022, [Online]. Available: http://arxiv.org/abs/2203.17055
  37. M. Karlbauer, T. Praditia, S. Otte, S. Oladyshkin, W. Nowak, and M. V. Butz, “Composing Partial Differential Equations with Physics-Aware Neural Networks,” Baltimore, USA, 2022.
  38. S. Hermann and J. Fehr, “Documenting research software in engineering science,” Scientific Reports, vol. 12, no. 1, Art. no. 1, 2022, doi: 10.1038/s41598-022-10376-9.
  39. A. H. Ludwig-Słomczyńska and M. Rehm, “Mitochondrial genome variations, mitochondrial-nuclear compatibility, and their association with metabolic diseases,” Obesity, 2022, doi: 10.1002/OBY.23424.
  40. S. Shuva, P. Buchfink, O. Röhrle, and B. Haasdonk, “Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue,” in Large-Scale Scientific Computing, 2022, pp. 402--409.
  41. C. Brecher et al., “Commitment zu aktivem Daten- und -softwaremanagement in großen Forschungsverbünden,” Bausteine Forschungsdatenmanagement, 2022, doi: 10.17192/BFDM.2022.1.8412.
  42. C. Brecher et al., “Commitment zu aktivem Daten- und -softwaremanagement in großen Forschungsverbünden,” 2022, doi: 10.17192/BFDM.2022.1.8412.
  43. C. C. Horuz et al., “Inferring Boundary Conditions in Finite Volume Neural Networks.”
  44. V. Zaverkin, G. Molpeceres, and J. Kästner, “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion,” Mon. Not. R. Astron. Soc., vol. 510, no. 2, Art. no. 2, 2022, doi: 10.1093/mnras/stab3631.
  45. T. Wenzel, M. Kurz, A. Beck, G. Santin, and B. Haasdonk, “Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows,” in Large-Scale Scientific Computing, Cham, 2022, pp. 410--418.
  46. T. Wenzel, G. Santin, and B. Haasdonk, “Stability of convergence rates: Kernel interpolation on non-Lipschitz domains.” arXiv, 2022. doi: 10.48550/ARXIV.2203.12532.
  47. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of target data-dependent greedy kernel algorithms: Convergence rates for $f$-, $f P$- and $f/P$-greedy.” arXiv, 2021. doi: 10.48550/ARXIV.2105.07411.
  48. P. Gavrilenko et al., “A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows,” in Large-Scale Scientific Computing, Cham, 2022, pp. 378--386.
  49. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution,” Journal of Approximation Theory, vol. 262, p. 105508, 2021, doi: https://doi.org/10.1016/j.jat.2020.105508.
  50. B. Haasdonk, T. Wenzel, G. Santin, and S. Schmitt, “Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods,” in Numerical Mathematics and Advanced Applications ENUMATH 2019, Cham, 2021, pp. 499--508.
  51. L. Eirich, M. Münch, D. Jäckle, M. Sedlmair, J. Bonart, and T. Schreck, “RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines,” Computer Graphics Forum (CGF), p. 14, 2022, doi: https://doi.org/10.1111/cgf.14452.
  52. L. Eirich, M. Münch, D. Jäckle, M. Sedlmair, J. Bonart, and T. Schreck, “RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines,” 2022.
  53. P. Gebhardt, X. Yu, A. Köhn, and M. Sedlmair, “MolecuSense: Using Force-Feedback Gloves for Creating and Interacting  with Ball-and-Stick Molecules in VR.” 2022. [Online]. Available: http://arxiv.org/abs/2203.09577
  54. P. Buchfink, S. Glas, and B. Haasdonk, “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds.” 2021. doi: https://doi.org/10.48550/arXiv.2112.10815.
  55. K. Cheng, Z. Lu, S. Xiao, S. Oladyshkin, and W. Nowak, “Mixed covariance function Kriging model for uncertainty quantification,” International Journal for Uncertainty Quantification, vol. 12, no. 3, Art. no. 3, 2022, doi: 10.1615/Int.J.UncertaintyQuantification.2021035851.
  56. B. Maier and M. Schulte, “Mesh generation and multi-scale simulation of a contracting muscle–tendon complex,” Journal of Computational Science, vol. 59, p. 101559, 2022, doi: https://doi.org/10.1016/j.jocs.2022.101559.
  57. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2022. doi: https://doi.org/10.48550/arXiv.2203.10061.
  58. T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf, “Visualization-based improvement of neural machine translation,” Computers & Graphics, vol. 103, pp. 45–60, 2022, doi: https://doi.org/10.1016/j.cag.2021.12.003.
  59. V. Zaverkin, D. Holzmüller, R. Schuldt, and J. Kästner, “Predicting properties of periodic systems from cluster data: A case study of liquid water,” The Journal of Chemical Physics, vol. 156, no. 11, Art. no. 11, 2022, doi: 10.1063/5.0078983.
  60. D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart, “A Framework and Benchmark for Deep Batch Active Learning for Regression,” arXiv:1112.5745, 2022.
  61. D. Holzmüller and D. Pflüger, “Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework,” in Sparse Grids and Applications - Munich 2018, Cham, 2021, pp. 69--100.
  62. M. Schneider, K. Weishaupt, D. Gläser, W. M. Boon, and R. Helmig, “Coupling staggered-grid and MPFA finite volume methods for free flow/porous-medium flow problems,” Journal of Computational Physics, vol. 401, p. 109012, 2020, doi: 10.1016/j.jcp.2019.109012.
  63. A. D. Beck, J. Zeifang, A. Schwarz, and D. G. Flad, “A neural network based shock detection and localization approach for discontinuous Galerkin methods,” Journal of Computational Physics, vol. 423, p. 109824, 2020, doi: 10.1016/j.jcp.2020.109824.
  64. P. Mossier, A. Beck, and C.-D. Munz, “A p-adaptive discontinuous Galerkin method with hp-shock capturing,” Joural of Scientific Computing, vol. 91, no. 4, Art. no. 4, 2022, doi: 10.1007/s10915-022-01770-6.
  65. A. Arad, D. Katoshevski, V. Vaikuntanathan, M. Ibach, J. B. Greenberg, and B. Weigand, “Longitudinal and Lateral Grouping in Droplet Streams using the Eulerian-Lagrangian Approach,” 2021.
  66. J. S. Lee, W.-S. Ko, and B. Grabowski, “Atomistic simulations of the deformation behavior of an Nb nanowire embedded in a NiTi shape memory alloy,” Acta Materialia, vol. 228, p. 117764, 2022, doi: 10.1016/j.actamat.2022.117764.
  67. I. Novikov, B. Grabowski, F. Körmann, and A. Shapeev, “Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe,” npj Computational Materials, vol. 8, no. 1, Art. no. 1, 2022, doi: 10.1038/s41524-022-00696-9.
  68. X. Zhang, S. V. Divinski, and B. Grabowski, “Ab initio prediction of vacancy energetics in HCP Al-Hf-Sc-Ti-Zr high entropy alloys and the subsystems,” Acta Materialia, vol. 227, p. 117677, 2022, doi: 10.1016/j.actamat.2022.117677.
  69. M. Alkämper, J. Magiera, and C. Rohde, “An Interface Preserving Moving Mesh in Multiple SpaceDimensions,” Computing Research Repository, vol. abs/2112.11956, 2021, [Online]. Available: https://arxiv.org/abs/2112.11956
  70. T. Martin and F. Allgöwer, “Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation,” Atlanta, GA, USA, 2022.
  71. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” Preprint:  arXiv:2108.11298, 2022.
  72. M. Hirche, P. N. Köhler, M. A. Müller, and F. Allgöwer, “Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems,” in Proc. 59th IEEE Conf. on Decision and Control (CDC), Jeju Island, Republic of Korea, 2020, pp. 1248–1253. doi: 10.1109/CDC42340.2020.9303838.
  73. S. Yu, M. Hirche, Y. Huang, H. Chen, and F. Allgöwer, “Model predictive control for autonomous ground vehicles: a review,” Auton. Intell. Syst., vol. 1, p. 4, 2021, doi: 10.1007/s43684-021-00005-z.
  74. J. Kneifl, D. Grunert, and J. Fehr, “A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning,” International Journal for Numerical Methods in Engineering, Apr. 2021, doi: 10.1002/nme.6712.
  75. M. Hertneck and F. Allgöwer, “Dynamic self-triggered control for nonlinear systems based on hybrid Lyapunov functions,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 533–539. doi: 10.1109/CDC45484.2021.9682784.
  76. S. Schlor, M. Hertneck, S. Wildhagen, and F. Allgöwer, “Multi-party computation enables secure polynomial control based solely on secret-sharing,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 4882–4887. doi: 10.1109/CDC45484.2021.9683026.
  77. R. Strässer, J. Berberich, and F. Allgöwer, “Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 4344–4351. doi: 10.1109/CDC45484.2021.9683211.
  78. T. Holicki and C. W. Scherer, “Algorithm Design and Extremum Control: Convex Synthesis due to Plant Multiplier Commutation,” in Proc. 60th IEEE Conf. Decision and Control, 2021, pp. 3249–3256. doi: 10.1109/CDC45484.2021.9683012.
  79. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” 2022.
  80. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” 2022.
  81. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” 2022.
  82. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” 2021.
  83. S. Aseyednezhad, L. Yan, M. Hassanizadeh, A. Raoof, and others, “An accurate reduced-dimension numerical model for evolution of electrical potential and ionic concentration distributions in a nano-scale thin aqueous film,” Advances in Water Resources, vol. 159, pp. 1--9, 2022, doi: 10.1016/j.advwatres.2021.104058.
  84. N. Seetha and S. M. Hassanizadeh, “A two-way coupled model for the co-transport of two different colloids in porous media,” Journal of Contaminant Hydrology, vol. 244, p. 103922, 2022, doi: 10.1016/j.jconhyd.2021.103922.
  85. L. Boumaiza et al., “Predicting vertical LNAPL distribution in the subsurface under the fluctuating water table effect,” Groundwater Monitoring & Remediation, 2022, doi: 10.1111/gwmr.12497.
  86. D. Alonso-Orán, C. Rohde, and H. Tang, “A local-in-time theory for singular SDEs with applications to fluid models with transport noise,” arXiv preprint arXiv:2010.09972, pp. 1–32, 2020, doi: 10.1007/s00332-021-09755-9.
  87. D. Alonso-Orán, C. Rohde, and H. Tang, “A Local-in-Time Theory for Singular SDEs with Applications to Fluid Models with Transport Noise,” Journal of Nonlinear Science, 2021, doi: https://doi.org/10.1007/s00332-021-09755-9.
  88. A. Schlaich, D. Jin, L. Bocquet, and B. Coasne, “Electronic screening using a virtual Thomas--Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces,” Nature Materials, 2021, doi: 10.1038/s41563-021-01121-0.
  89. L. Boumaiza et al., “Predicting Vertical LNAPL Distribution in the Subsurface under the Fluctuating Water Table Effect,” Groundwater Monitoring & Remediation, vol. n/a, no. n/a, Art. no. n/a, 2022, doi: https://doi.org/10.1111/gwmr.12497.
  90. W. Wang, G. Yang, C. Evrim, A. Terzis, R. Helmig, and X. Chu, “An assessment of turbulence transportation near regular and random permeable interfaces,” Physics of Fluids, vol. 33, p. 115103, 2021, doi: 10.1063/5.0069311.
  91. N. Seetha and S. M. Hassanizadeh, “A two-way coupled model for the co-transport of two different colloids in porous media,” Journal of Contaminant Hydrology, vol. 244, p. 103922, 2022, doi: 10.1016/j.jconhyd.2021.103922.
  92. S. Aseyednezhad, L. Yan, S. M. Hassanizadeh, and A. Raoof, “An accurate reduced-dimension numerical model for evolution of electrical potential and ionic concentration distributions in a nano-scale thin aqueous film,” Advances in Water Resources, vol. 159, p. 104058, 2022, doi: 10.1016/j.advwatres.2021.104058.
  93. T. Yi, X. Chu, B. Wang, J. Wu, and G. Yang, “Numerical simulation of single bubble evolution in low gravity with fluctuation,” International Communications in Heat and Mass Transfer, vol. 130, p. 105828, 2022, doi: 10.1016/j.icheatmasstransfer.2021.105828.
  94. M. Schneider, K. Weishaupt, D. Gläser, W. M. Boon, and R. Helmig, “Coupling staggered-grid and MPFA finite volume methods for free flow/porous-medium flow problems,” Journal of Computational Physics, vol. 401, p. 109012, 2020, doi: 10.1016/j.jcp.2019.109012.
  95. T. Koch et al., “DuMux 3--an open-source simulator for solving flow and transport problems in porous media with a focus on model coupling,” Computers & Mathematics with Applications, vol. 81, pp. 423--443, 2021, doi: 10.1016/j.camwa.2020.02.012.
  96. M. Kurz and A. Beck, “A machine learning framework for LES closure terms,” ETNA - Electronic Transactions on Numerical Analysis, pp. 117–137, Sep. 2020, doi: 10.1553/etna_vol56s117.
  97. J. Steigerwald, M. Ibach, J. Reutzsch, and B. Weigand, “Towards the Numerical Determination of the Splashing Threshold of Two-Component Drop Film Interactions,” in High Performance Computing in Science and Engineering’20, Springer, 2021, pp. 261--279. doi: 10.1007/978-3-030-80602-6_17.
  98. E. Coltman, M. Lipp, A. Vescovini, and R. Helmig, “Obstacles, Interfacial Forms, and Turbulence: A Numerical Analysis of Soil--Water Evaporation Across Different Interfaces,” Transport in Porous Media, vol. 134, no. 2, Art. no. 2, 2020, doi: 10.1007/s11242-020-01445-6.
  99. X. Chu, W. Wang, G. Yang, A. Terzis, R. Helmig, and B. Weigand, “Transport of turbulence across permeable interface in a turbulent channel flow: interface-resolved direct numerical simulation,” Transport in Porous Media, vol. 136, no. 1, Art. no. 1, 2021, doi: 10.1007/s11242-020-01506-w.
  100. S. Hasan et al., “Direct characterization of solute transport in unsaturated porous media using fast X-ray synchrotron microtomography,” Proceedings of the National Academy of Sciences, vol. 117, no. 38, Art. no. 38, 2020, doi: 10.1073/pnas.2011716117.
  • Since Oct. 2021: Independent Junior Research Group Leader with SimTech, University of Stuttgart/Germany
  • Jul-Dec 2020: Senior Postdoctoral Researcher/Group Leader, Insitute for Computational Physics, University of Stuttgart/Germany (Supervisor: Christian Holm)
  • 2017-2020: Postdoctoral Researcher, Laboratoire Interdisciplinaire de Physique (LiPhy), CNRS, Université Grenoble-Alpes/France (Supervisor: Benoit Coasne)
  • 2012-2017: Doctoral researcher, Freie Univerität Berlin/Germany (Supervisor: Roland R. Netz)
  • 2005-2012: Studies of physics (Dipl.-Phys.), University of Stuttgart/Germany
  • Computer simulations of complex liquids at interfaces and in confinement.
  • Monte-Carlo, Molecular Dynamics and advanced free energy methods.
  • Statistical physics and classical thermodynamics at the interface between chemical physics, physical chemistry, materials science and biology.
  • Coarse-graining and implicit solvent methods.
  • Data-driven approaches for effective properties of hierarchical porous materials.

to be listed soon

EXC

  • Participating Researcher
  • Junior Research Group Leader
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