Many mathematical modeling scenarios involve an inherent level of uncertainty.
Recently it has been a common practice to incorporate noise in deterministic systems to represent random effects and build models closer to reality. There is growing interest in adding stochastic terms to mathematical models in neuroscience, epidemiology, biology, physics, engineering, finance, and many other areas. In this context, stochastic differential equations (theory derived from the intersection of random processes and differential equations) are widely used in the study of phenomena subject to noise and fluctuations of a different nature, and in practical situations where uncertainty plays an important role.
This lecture will present an introduction to these types of equations, their various applications and methods for their numerical-computational simulation. We will also discuss some current research questions in the area.
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*Participants: will not be able to access FGV premises wearing shorts, flip-flops, top or cropped blouse, miniskirt or tank top. The use of the Mask and the presentation of proof of vaccination (physical or digital) will be mandatory.
Apoiadores / Parceiros / Patrocinadores
Hugo Alexander de la Cruz
Hugo Alexander de la Cruz holds a degree in Mathematics from the Universidad de Oriente (1998), a PhD in Mathematics from the Universidad de la Habana (2007), a postdoctoral degree from the Institut Mittag-Leffler, Royal Swedish Academy of Sciences, Sweden (2007) and a postdoctoral degree from the IMPA- National Institute of Pure and Applied Mathematics (2009-2013).
He is currently Professor and Postgraduate Coordinator at the School of Applied Mathematics (EMAp) at FGV. Has experience in Mathematics, with emphasis on Stochastic Differential Equations, Numerical Analysis, Stochastic Processes and Computer Simulation. Since 2018 he is Associate Editor of the journal "Applied Numerical Mathematics" (APNUM) of the International Association for Mathematics and Computers in Simulation (IMACS).