Computational Mechanics (CM)

Overview

Numerical simulations are playing an increasingly important role in scientific investigations and industrial innovation. The ability to study a full range of physical and temporal scales using virtual models allows today to rapidly explore innovative technological solutions, simulate the behavior of complex biological or artificial systems, devise new production processes, optimize components and discover new materials with innovative properties. This research is having an increasingly significant impact on industrial competitiveness, where not only virtual prototyping based on numerical simulations is considered the cornerstone to reduce the time and costs required by experimentation for the development of new reliable and high-quality products, but digital twins are developed to predict the behavior of components or processes throughout their operational life. 

The development of numerical simulation tools is an activity that requires skills that come from different fields: mechanics, fundamental to select the most suitable physical models, mathematics, necessary to formalize the models in governing equations and subsequently to identify the most suitable solution algorithms, computer science, which finally allows the implementation of such algorithms in efficient and robust programs. 

Compared to traditional doctoral programs mainly focused on one of these disciplines, the Track in Computational Mechanics (CM) offers a markedly interdisciplinary doctoral training for graduates who wish to specialize in the research and development of innovative numerical simulation methods for the analysis of complex systems of high technological interest or for their application to frontier emergent topics. The study plan builds on a series of foundational courses to provide a solid background in applied mathematics, numerical analysis, computer science, mechanics, dynamic systems and control, machine learning techniques. These courses are complemented by advanced courses and specialized research seminars to address a wide variety of complex engineering problems concerning:

•  Computational solid and fluid mechanics

•  Computational Materials Science

•  Tribology and surface engineering

•  Computational mechanics of fracture and damage

•  Coupled problems (multi-scale and multi-physics)

•  Fluid-structure interaction

•  Biomechanics and bioengineering

•  Problems of shape optimization and automatic control for mechanics

•  Data-driven models

•  Machine learning and artificial intelligence algorithms in computational mechanics

•  Numerical techniques for large-scale problems

•  Reliability and durability of composites and heterogeneous materials

•  Characterization and simulation of metamaterials

•  Integrated technical-economic analysis and the life cycle of materials

•  Recycled materials and hybrid composites

•  Applications to renewable energies (hydrogen, photovoltaics, etc.)

•  Quantitative methods for cultural heritage (compatibility of materials for restoration, computational archaeology, etc.)

Structure of the study program, research facilities, and international collaborations 

Input and output profiles 

Prospective students should preferably have a background in engineering, mathematics, computer science, physics, statistics or a related field. Potential students are encouraged to propose their own research topics. The School is looking for highly motivated people.

The CM track prepares researchers and professionals capable of analyzing and proposing solutions to various real problems of industrial, economic and social interest, making them qualified to work in high-profile professional roles within universities and research centers. Virtual testing and digital twins are some key enabling technologies for the digital transformation of industry, and are therefore of high value for the job market (industry, services, public and private research laboratories, study centers, regulatory centers, consulting firms, and the public sector).

So far, alumni and former collaborators had an excellent placement in universities and in R&D departments of companies.