Parameters or functions used in the modelling of systems and processes for several relevant applications in engineering and science may be unknown.
However, partial experimental data on observable indirect quantities may be available. Using the inverse problem approach there is a link between models and experiments, allowing the determination of the unknown parameters of interest. The parameters and functions involved in the models present random variations and the experimental data is always contaminated by random noise. Such characteristics should be taken into account in the formulation and solution of inverse problems.
In this talk, inverse problems in two different application areas, namely radiative transfer and anomalous diffusion, are presented using formulations based on computational intelligence and Bayesian inference.
About Antônio José da Silva Neto
Antônio J. Silva Neto is a Professor in the Department of Mechanical Engineering and Energy at Polytechnic Institute, Universidade do Estado do Rio de Janeiro (Rio de Janeiro State University). He has a diverse international educational background in mechanical/nuclear engineering and applied mathematics: PhD in Mechanical Engineering (North Carolina State University – NCSU, USA, 1993), with a minor in Computational Mathematics; MSc in Nuclear Engineering (Universidade Federal do Rio de Janeiro – UFRJ, Brazil, 1989); and BSc Mechanical/Nuclear Engineering (UFRJ, Brazil, 1983).
He worked for the Brazilian National Commission on Nuclear Energy, CNEN (1984-1986), Promon Engineering, the largest Brazilian private company in Consulting Engineering (1986-1997), and since 1997 works for UERJ, being a Professor since 2013. His multi/interdisciplinary research activities resulted in the publication of 13 books, 54 book chapters, 135 refereed journal papers, and 417 full scientific conference papers. He has also supervised 20 visiting and postdoctoral fellows, 27 DSc, 47 MSc and 95 undergraduate level students. Professor Silva Neto main current research areas are Inverse Problems; Heat and Mass Transfer; Radiative Transfer; Computational Intelligence; Environmental Modelling.
Acknowledgements: UNIC/Cyprus, Erasmus +, and Brazilian agencies FAPERJ, CNPq and CAPES