Scientific Computing

Versión en Español


The main objective of this area is to develop and apply models able to predict the behavior of systems in science and engineering.

Reseach lines

  • Parallel processing of models of systems in science and engineering.
  • Numerical methods of partial differential equations.
  • Specific applications for modelling oil deposits for their efficient explotation and flow and transport modelling of underground water.
  • Category theory with applications to languages, programming, and data. Multilevel systems, multigraph. Information systems. Petri nets.
  • Scientific computing with Matematica, Maple, for automating deduction for computing differential forms (differential equations as exterior, ideal, and Cartan systems), in areas such as physics and engineering. Symbolic computing.
  • Quantic computing, quantic algoritms.
  • Multivalued fuzzy logic, rough sets, evidence theory, confidence theory, posibility theory.
  • Algorithmic problems of algebra theory and integer number theory.
  • Symbolic computing for associative algebras and Lie algebras.
  • Quantifications of Lie algebras and their applications in quantic group theory with computational methods.
  • Theory of Galois for associative algebras with computational methods.
  • Free algebras ant their automorphisms with computational methods.
  • Ring theory with computational methods.
  • Computational models and algorithms for fractal structure generation.
  • Computational model of the bi-color DLA.
  • Computational models in natural science.
  • Computational algorithms and multi-thread programming in Windows and Linux.
  • Advanced programming in C++ and its application implementing computational algorithms.
  • Scientific visualization with C++ Builder, OpenGL and other computer packages.


¤ Masters and Doctoral

¤ Masters