Scientific Publications

Song, J.; Kannan, V.; Shishkina, O.; Zhu, X.: Direct numerical simulations of the transition between rotation- to buoyancy-dominated regimes in rotating Rayleigh–Bénard convection. International Journal of Heat and Mass Transfer 232, 125971 (2024)

Minnich, A.; Herbst, H.; Herminghaus, S.; Kneib, T.; Wacker, B.; Schlüter, J. C.: A behavioral economic perspective on demand responsive transportation. Transportation Research Interdisciplinary Perspectives 27, 101176 (2024)

Sharma, P.; Herminghaus, S.; Heuer, H.; Heidemann, K. M.: Impact of the density of line service stations on overall performance in Bi-modal public transport settings. Multimodal Transportation 3 (3), 100118 (2024)

Yang, R.; van den Ham, T.; Verzicco, R.; Lohse, D.; Huisman, S. G.: Circular objects do not melt the slowest in water. Physical Review Fluids 9, 083501 (2024)

Hosaka, Y.; Chatzittofi, M.; Golestanian, R.; Vilfan, A.: Chirotactic response of microswimmers in fluids with odd viscosity. Physical Review Research 6, L032044 (2024)

Rudelt, L.; González Marx, D.; Spitzner, F. P.; Cramer, B.; Zierenberg, J.; Priesemann, V.: Signatures of hierarchical temporal processing in the mouse visual system. PLOS Computational Biology 20 (8), e1012355 (2024)

Chatzittofi, M.; Agudo-Canalejo, J.; Golestanian, R.: Nonlinear response theory of molecular machines. EPL 147 (2), 21002 (2024)

Wei, X.; Junot, G.; Golestanian, R.; Zhou, X.; Wang, Y.; Tierno, P.; Meng, F.: Molecular dynamics simulations of microscopic structural transition and macroscopic mechanical properties of magnetic gels. The Journal of Chemical Physics 161, 074902 (2024)

Wei, D.; Yang, Y.; Wei, X.; Golestanian, R.; Li, M.; Meng, F.; Peng, Y.: Scaling Transition of Active Turbulence from Two to Three Dimensions. Advanced Science (2024)

Berscheid, A.; Straetener, J.; Schilling, N. A.; Ruppelt, D.; Konnerth, M. C.; Schittek, B.; Krismer, B.; Peschel, A.; Steinem, C.; Grond, S. et al.; Brötz-Oesterhelt, H.: The microbiome-derived antibacterial lugdunin acts as a cation ionophore in synergy with host peptides. mBio (2024)

Cheng, Z.; Vilfan, A.; Wang, Y.; Golestanian, R.; Meng, F.: Near-field hydrodynamic interactions determine travelling wave directions of collectively beating cilia. Journal of the Royal Society Interface 21 (217), 20240221 (2024)

Rana, N.; Golestanian, R.: Defect Solutions of the Nonreciprocal Cahn-Hilliard Model: Spirals and Targets. Physical Review Letters 133 (7), 078301 (2024)

Kokkoorakunnel, R.; Saha, S.; Golestanian, R.: Anomalous Fluctuations in a Droplet of Chemically Active Colloids or Enzymes. Physical Review Letters 133, 058401 (2024)

Song, J.; Shishkina, O.; Zhu, X.: Direct numerical simulations of rapidly rotating Rayleigh–Bénard convection with Rayleigh number up to 5 x 10¹³. Journal of Fluid Mechanics 989, A3 (2024)

Zhu, X.; Chen, Y.; Chong, K.L.; Lohse, D.; Verzicco, R.: A boundary condition-enhanced direct-forcing immersed boundary method for simulations of three-dimensional phoretic particles in incompressible flows. Journal of Computational Physics 509, 113028 (2024)

Osat, S.; Metson, J.; Kardar, M.; Golestanian, R.: Escaping Kinetic Traps Using Nonreciprocal Interactions. Physical Review Letters 133, 028301 (2024)

Oróstica, K. Y.; Mohr, S. B.; Dehning, J.; Bauer, S.; Medina-Ortiz, D.; Iftekhar, E. N.; Mujica, K.; Covarrubias, P. C.; Ulloa, S.; Castillo, A. E. et al.; Daza-Sánchez, A.; Verdugo, R. A.; Fernández, J.; Olivera-Nappa, Á.; Priesemann, V.; Contreras, S. A.: Early mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variants in Chile. Scientific Reports 14, 16000 (2024)

Suth, D.; Luther, S.; Lilienkamp, T.: Chaos control in cardiac dynamics: terminating chaotic states with local minima pacing. Frontiers in Network Physiology 4, 1401661 (2024)

Golovin, A.; Mölter, J.; Kuehn, C.: Polyadic Opinion Formation: The Adaptive Voter Model on a Hypergraph. Annalen der Physik 536 (7), 2300342 (2024)

Kiemen, A. L.; Wu, P.-H.; Braxton, A. M.; Cornish, T. C.; Hruban, R. H.; Wood, L. D.; Wirtz, D.; Zwicker, D.: Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings. Science Advances 10 (30), eado5103 (2024)