activity
20172019
most citedExperimental data over quantum mechanics simulations for inferring the repulsive exponent of the Lennard-Jones potential in Molecular Dynamics

3 citations · 3 across the 1 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2019

Optimal sensing for fish school identification

Pascal Weber, Georgios Arampatzis, Guido Novati +3

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are…

physics.flu-dyn2019

Optimal sensor placement for artificial swimmers

Siddhartha Verma, Costas Papadimitriou, Nora Luethen +2

Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the vis…

physics.comp-ph2018

Bayesian selection for coarse-grained models of liquid water

Julija Zavadlav, Georgios Arampatzis, Petros Koumoutsakos

The necessity for accurate and computationally efficient representations of water in atomistic simulations that can span biologically relevant timescales has born the necessity of…

math.PR2018

-Leaping: An adaptive, accelerated stochastic simulation algorithm, bridging -leaping and -leaping

Jana Lipková, Georgios Arampatzis, Philippe Chatelain +2

We propose the -leaping algorithm for the acceleration of Gillespie's stochastic simulation algorithm that combines the advantages of the two main accelerated methods; the -l…

physics.chem-ph20173 cited

Experimental data over quantum mechanics simulations for inferring the repulsive exponent of the Lennard-Jones potential in Molecular Dynamics

Lina Kulakova, Georgios Arampatzis, Panagiotis Angelikopoulos +3

The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The potential models atomi…