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Multiscale and hybrid algorithms for stochastic chemical kinetics

Posted on April 18, 2012January 15, 2013 By admin No Comments on Multiscale and hybrid algorithms for stochastic chemical kinetics
Multiscale and hybrid algorithms for stochastic chemical kinetics

Many biochemical network models display scale separation with respect to reaction rates and/or molecular copy numbers. Depending on the type of question under study, different models are best suited to simulate the system. For some parts of the system, a macroscopic model might be appropriate. For other parts, a mesoscopic model may provide additional insight…

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Multiscale methods, Reaction Diffusion Master Equation, Stochastic Chemical Kinetics

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Data-and simulation-driven life science. Much of our work in eScience and applied ML has applications in life science, and in Systems Biology in particular. We aim to enable data-and simulation-driven scientific discovery.

HASTE - a cloud native framework for intelligent processing of image streams: http://haste.research.it.uu.se/

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Decentralized AI, Federated Learning. One focus area of the group is development of methods and software to address decentralized and privacy-preserving AI. We are core contributors to the FEDn open source framework for scalable federated machine learning:

https://github.com/scaleoutsystems/fedn
Introduction to Federated Learning by Andreas Hellander
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https://www.scaleoutsystems.com/

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