cselab – Page 3

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/

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Archives

Our paper “Optimal allocation of limited test resources for the quantification of COVID-19 infections” was published in Swiss Medical Weekly

Professor Koumoutsakos selected for the Board on Mathematical Sciences and Analytics (BMSA)

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

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

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

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

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

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

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

Korali our framework for uncertainty quantification and optimization released