1- Theoretical aspects and fundamentals

(a) mathematical theory and formulation of inverse and optimization problems, (b) regularization techniques, (c) model order reduction, (d) identification problems, (e) sensitivity analysis, (f) new approaches.

 2- Algorithms

(a) reconstruction techniques, (b) deterministic, stochastic and hybrid techniques, (c) multi-objective and multi-level optimization,  (d)  heuristic  approaches,  (e)  design  of  experiments,  (f)  constraint treatment,  (g) robust optimization  under uncertainty, (h) objective functions and direct problems, (i) numerical efficiency, (j) coupled problems; (k) new techniques.

 3- Applications

(a) optimal design in electrical and electronic  engineering, (b) optimization in information and communication systems,(c) non destructive evaluation, (d) industrial and biomedical tomography, (e) optimization in measurement systems, (f) optimization and inverse problems in biomedical engineering, (g) control systems, (h) large scale systems, (i) mechatronics, (j) nano- and microsystems, (k) renewable energy, (l) benchmark problems, (m) other applications.

 4- Software methodologies

(a)  parallel  and  distributed  computing,  GPU  computing,  (b)  soft  computing  and  artificial  intelligence (c)  new methodologies.