Kyushu University Academic Staff Educational and Research Activities Database
Researcher information (To researchers) Need Help? How to update
PIERLUIGI CESANA Last modified date:2022.05.29

Administration Post

E-Mail *Since the e-mail address is not displayed in Internet Explorer, please use another web browser:Google Chrome, safari.
 Reseacher Profiling Tool Kyushu University Pure
Academic Degree
PhD in Applied Mathematics, SISSA International School for Advanced Studies, Italy
Country of degree conferring institution (Overseas)
Yes Doctor
Field of Specialization
Calculus of Variations
Total Priod of education and research career in the foreign country
Outline Activities
My research topics lie at the intersection of materials science and mathematical analysis. I have been mostly working on mathematical modeling of plasticity and phase transformations in Shape-Memory Alloys. I am currently working on designing molecules at the nano-scale by blending AI and quantum mechanics methods.
Research Interests
  • Automatized material discovery with the aid of AI and Machine Learning techniques.
    keyword : Artificial Intelligence, Machine Learning, polymers
  • Understanding strengthening in mille-feuille structures via mesoscale modeling of structural and material instabilities
    keyword : Kink formation, disclination, dislocation, crystal plasticity, Gamma-convergence, Finite Element
  • Mesoscale modeling for disclinations toward a theory for kink and material strengthening
    keyword : Mille-feuille structure, kink, disclination
  • Nematic Elastomers. Modeling and analysis of order-strain interaction in soft crystalline polymers elastic foundations.
    keyword : nematic liquid crystal elastomers
  • Martensite. Modeling of topological defects in crystal lattices by means of stochastic models.
    keyword : calculus of variations, martensite
Current and Past Project
  • Prepared and submitted a paper "A probabilistic model for interfaces in a martensitic phase transition" with Prof. B. Hambly.
    Started new project on modeling martensitic microstructure with fragmentation models and computation of power laws of geometric quantities generated during the fragmentation process.
    Started new research line on modeling disclinations and kink formation with Prof. Tomonari Inamura (TokyoTech, visiting Oxford during 2018).
  • Modeling of disclinations and kink formation in metals
  • Modeling and Analysis of Nematic Elastomers and Martensite in various configuration with both variational and probabilistic methods.

    Computed exact exponents of power law distributions from fragmentation models describing simple mechanisms of space-time evolutions of martensitic microstructures. Compared analytical and numerical results. Collaboration with B. Hambly (Oxford).

    Computed exact effective energies of nematic elastomers in elastic foundations configuration, in the membrane regime with small displacements. Obtained phase-diagrams for the energy in the dependence of various geometric and material parameters. Collaboration with A. Baldelli (CNRS)

    Obtained exact solutions to 4th order reaction-diffusion equations for surface growth and diffusion. Collaboration with the La Trobe group of P. Broadbridge, D. Triadis and D. Gallage.

    Obtained the exact relaxation of an energy model for the planar hexagonal-to-orthorhombic martensitic transformation. One book chapter published.

Academic Activities
Educational Activities
Guidance for graduate course (6-10 students per year on average)
・ International Science Course (12 students per year on average)
・ Supervision of doctoral and master's students (project research)
・ Chairman of the international students affairs, 2018-to date
Professional and Outreach Activities
International collaboration between IMI and Oxford University on stochastic models of martensitic transformation
・ Design of shape memory alloys supported by AIRIMAQ Fellowship
International joint research between IMI-CNRS (Paris): Application to design of soft robots, sensors, actuators, prostheses, etc.
For Daicel Co., Ltd., designing new molecules using machine learning; consulting and guidance.