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Conference papers
Autonomous learning of parameters in differential equations
Abstract : We propose EDEN+ 1 a fully automatic learner of parameters in dynamical systems that selects automatically the next experiment to do in the laboratory to improve its performance. EDEN+ improves upon EDEN, an experimental design algorithm proposed in the context of DREAM 6 and 7 challenges, with several new features: ability to take into account experiments with different costs, Monte-Carlo Tree Search parallelization, global optimization for parameter estimation. An illustration of the behaviour of EDEN+ is given on one of the DREAM7 challenging problems.
Cited literature [12 references]
https://hal.archives-ouvertes.fr/hal-01272370
Contributor : Veronique Letort <>
Submitted on : Wednesday, February 10, 2016 - 4:54:31 PM
Last modification on : Monday, January 25, 2021 - 4:16:03 PM
Long-term archiving on: : Saturday, November 12, 2016 - 5:20:18 PM
Contributor : Veronique Letort <>
Submitted on : Wednesday, February 10, 2016 - 4:54:31 PM
Last modification on : Monday, January 25, 2021 - 4:16:03 PM
Long-term archiving on: : Saturday, November 12, 2016 - 5:20:18 PM
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AutoML_soumis.pdf
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