Our paper “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100 000” was published in the Journal of Fluid Mechanics [PDF].
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Our paper “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100 000” was published in the Journal of Fluid Mechanics [PDF].
Read MoreOur paper “Robust optimal sensor configuration using the value of informations” was published in Structural Control and Health Monitoring [PDF].
Read MoreOur paper “Multiscale simulations of complex systems by learning their effective dynamics” was published in Nature Machine Intelligence [PDF].
Read MoreOur paper “Modelling glioma progression, mass effect and intracranial pressure in patient anatomy” was published in the Journal of the Royal Society Interface [PDF].
Read MoreOur paper “Scientific multi-agent reinforcement learning for wall-models of turbulent flows” was published in Nature Communications [PDF] and selected for their Editors’ Highlights webpage in the area of “Applied physics and mathematics“. The Editors’ Highlights pages aim to showcase the 50 best papers recently published in an area.
Read MoreOur paper “Computing foaming flows across scales: From breaking waves to microfluidics” was published in Science Advances [PDF] and is in the news: https://ethz.ch/en/news-and-events/eth-news/news/2022/02/new-method-cracks-simulation-of-foam-formation.html https://www.seas.harvard.edu/news/2022/02/bubbles-bubbles-everywhere
Read MoreOur paper “Nanopumps without pressure gradients: ultrafast transport of water in patterned nanotubes” was published and featured on the cover of Journal of Physical Chemistry B [PDF] [Cover]
Read MoreOur paper “Independent control and path planning of microswimmers with a uniform magnetic field” was published in Advanced Intelligent Systems [PDF]
Read MoreOur paper “Learning efficient navigation in vortical flow fields” was published in Nature Communications [PDF]
Read MoreOur paper “Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization” was published in Computer Methods in Applied Mechanics and Engineering [PDF]
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