Conferences

As a part of the The 2021 ASME/IEEE International Conference on Mechatronics and Embedded Systems and Applications (MESA)

FDTA (Symposium on Fractional Derivatives and Their Applications) was started by Prof. Om Agrawal and other FDTA colleagues in 2003 IDETC under the ASME DED VIB TC (Technical Committee), which meets on odd years under a DED TC. In 2007 and 2009, FDTA was under TC MSNDC. Starting from 2011, FDTA symposium is under MESA TC(under ASME DED) for better development of and better service to the FDTA community. ASME/IEEE MESA conference holds every odd year. MESA TC hosted FDTA2011 in Washington DC, FDTA2013 in Portland OR, FDTA2015in Boston, MA, and FDTA2017 in Cleveland, OH, and FDTA2019 in Anaheim, CA.

Please note that ASME DED MESA TC also hosts conferences under IEEE/ASME in even years (e.g. 2008 in Beijing, China (papers), 2010 in Qingdao, China (papers), 2012 in Suzhou, China (papers): 2014 in Italy http://mesa2014.org (papers), and 2016 in Aukland NZ, http://www.mesa2016.org/ (papers) and MESA2018 in Oulu, Finland) where FDTA symposia were also organized and papers were published in ieeeXplore.ieee.org  unfortunately, MESA2020 was cancelled due to COVID-19.

For 2021 FDTA Symposium under ASME/IEEE MESA2021, papers are solicited in the area of fractional derivatives and their applications. The subjects of the papers may include, but are not limited to,

• mathematical modeling of fractional and/or stochastic fractional dynamic systems in the real world, stability analysis and numerical techniques for these equations,
• fractional controller design and system identification,
• stability analysis of fractional systems, nonlinear and stochastic fractional dynamic systems,
• fractional order models and their experimental verifications, and applications of fractional models to engineering systems in general and mechatronic embedded systems in particular,
• fractional calculus based models for cyber-physical systems (CPS) and cyber-human systems (CHS), and in general, intelligent adaptive systems (IAS),
• fractional calculus based better characterization of complex systems in general,
• applied fractional calcuclus in big data analytics and variability quatification,
• applied fractional calculus in machine learning for more optimal way to optimize,
• fractional calculus based better characterization of complex systems in general ...

http://mechatronics.ucmerced.edu/FDTA