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Mathematical Analysis, Modelling, and Applications

The activity in mathematical analysis is mainly focussed on ordinary and partial differential equations, on dynamical systems, on the calculus of variations, and on control theory. Connections of these topics with differential geometry are also developed.The activity in mathematical modelling is oriented to subjects for which the main technical tools come from mathematical analysis. The present themes are multiscale analysismechanics of materialsmicromagneticsmodelling of biological systems, and problems related to control theory.The applications of mathematics developed in this course are related to the numerical analysis of partial differential equations and of control problems. This activity is organized in collaboration with MathLab for the study of problems coming from the real world, from industrial applications, and from complex systems.


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Research fields

  • geometry, in particular algebraic, differential, and noncommutative geometry, also with applications to quantum field and string theory
  • mathematical analysis, in particular calculus of variations, control theory, partial and ordinary differential equations
  • mathematical modelling, in particular mechanics of solids and fluids, modelling of complex and biological systems, multiscale analysis
  • mathematical physics, in particular integrable systems and their applications, nonlinear partial differential equations, mathematical aspects of quantum physics
  • numerical analysis and scientific computing, applied to partial differential equations and to control problems

PhD and MSC courses:


  • SISSA MathLab: a laboratory for mathematical modeling and scientific computing
  • SAMBA a laboratory in collaboration with the Cognitive Neuroscience Group

Area Coordinator


Former Faculty Members

Former Professors

Visiting Professors

Introductory lectures on mean curvature flow and minimal surfaces

  • Lesson 21/05: ROOM 134
  • Lesson 22/05: ROOM 133
  • Lesson 28/05: ROOM 
  • Lesson 29/05: ROOM 136


High Performance Computing for Data Science

The course will utilize a combination of frontal lectures and live programming demonstrations. The course will maintain a balance of approximately 50% frontal lectures and 50% hands-on sessions. The course is designed to be highly interactive, with ample opportunities for students to ask questions and engage in discussions during both the frontal lectures and hands-on sessions. Course materials and further details here.  


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