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Professor Koumoutsakos has been selected for the Board on Mathematical Sciences and Analytics (BMSA) of the National Academies of Sciences, Engineering and Medicine.

Our paper “Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries” was published in Swiss Medical Weekly [PDF]

Our paper “Bubbles in turbulent flows: Data-driven, kinematic models with history terms” was published in Multiphase Flow [PDF]

Our paper “Mirheo: high-performance mesoscale simulations for microfluidics” was published in Computer Physics Communications [PDF]

Our paper “Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics” was published in Neural Networks [PDF]

Our paper “Optimal Flow Sensing for Schooling Swimmers” was published in the Journal Biomimetics [PDF]

Our paper “Machine Learning for Fluid Mechanics” was published in Annual Review of Fluid Mechanics [PDF]

Our paper “A hybrid particle volume-of-fluid method for curvature estimation in multiphase flows” was published the International Journal of Multiphase Flow [PDF]

We are delighted to announce the first public version of Korali, our high-performance framework for Uncertainty Quantification and Optimization. Visit Korali’s webpage for installation and tutorials: https://www.cse-lab.ethz.ch/korali/

Our paper “Optimal sensor placement for artificial swimmers” was published in the Journal of Fluid Mechanics [PDF]