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Discrete & Continuous Dynamical Systems - S

Recent advances in mathematical methods for IoT and Data Assimilation

Editor: Emmanuel Frenod

Title: Recent advances in mathematical methods for IoT and Data Assimilation

Author submission link: https://ef.msp.org/submit/aims_dcds_s&cr=AdministerGratificationBanishedConfounding

Justification: IoT is a wide challenge in the next futur. Smart Industry, Smart Farming, Economics, Environment, Energy Business are - among others - strongly concerned.

Yet, advanced mathematics and statistics have a huge role to play in this challenge .
Indeed piloting of systems is composed of a navigation and a guiding steps.
Navigation step consists in synthesizing - at the fly - data coming from sensors in order to determine the state in which the thing to be piloted is. It requires to consider that those data are here to correct continuously a simulation led via a mathematical model describing the dynamics of the thing to be piloted.
The guiding step consists in using - with an optimization slant - the dynamical model to find the way to reach the target.

Hence the coupling of Continuous or Discrete Dynamical Systems with Data Flow is a mathematical issue. They are the ones to be addressed by this special issue. Theoretical aspects and Application are welcome

In particular, topics to be addressed include, but are not limited to:
- PDE simulations by ML methods
- ODE an PDE based Deep Learning Methods
- Data-Model-Coupling for IA
- ML use in Kalman filter
- Variational Data Assimilation
- Kalman Filter and variants
- implementations issues
- Real life, Scientific and Industrial Applications.

2019  Impact Factor: 1.233

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