Block D – “Computational Methods”, is conceived for eager students of Mechatronics Engineering coming from Engineering Sciences or Systems Engineering who are willing to complete their knowledge by acquiring advanced mathematical skills and computing capabilities.
Besides a first advanced course in mathematical analysis addressing complements of complex variable theory and elements of distribution theory, the block provides a second course introducing the Python exosystem for scientific computing along with a third course on data analysis and signal processing techniques for model fitting and parametric estimates or non-linear solving of inverse problems (such as image denoising, source separation, and PSF decomposition).
The fourth course will finally provide a solid knowledge of the theoretical background behind the main Machine Learning (ML) algorithms from Neural Networks to Reinforcement Learning, along with their application (by using the standard libraries in a Python environment) to open physical problems.
Block D aims to prepare professionals who can deal with complex design and managing problems using advanced mathematical tools, yet with an engineering attitude.
Professional opportunities involve R&D divisions in manufacturing or engineering companies that deal with complex computational problems or need advanced mathematical tools.
| Year | sem |
SPECIFIC SUBJECTS – Block D |
CFU | SSD | class hours |
| 1 | I | NUMERICAL METHODS FOR ASTROPHYSICS | 6 | FIS/05 | 60 |
| 1 | I | MATHEMATICAL METHODS FOR PHYSICS | 8 | FIS/02 | 80 |
| 2 | I | LABORATORY – CALCULUS | 4 | INF/01 | 40 |
| 2 | II | MACHINE LEARNING METHODS FOR PHYSICS | 6 | FIS/01 | 60 |
| TOTAL ECTS |
24 |
1st Year – I sem – II sem
2nd Year – I sem – II sem