Model analysis I

2021/2022
Programme:
Medical Physics, Second Cycle
Year:
1. year
Semester:
first
Kind:
optional
ECTS:
8
Language:
slovenian
Hours per week – 1. semester:
Lectures
2
Seminar
0
Tutorial
2
Lab
0
Prerequisites

Completed first level of studies (undergraduate studies).

Content (Syllabus outline)

Kinematic models (variational approach, linear programming, nonlinear minimization),
Population models and models of chemical kinetics (phase analysis, modeling of data and parameter estimation, method of normal matrix, singular value decomposition method),
Stochastic models (generators of random numbers, frequent distribution of probability for model analysis work, Monte Carlo integration, simulations, Metropolis algorithm),
Harmonic analysis (FFT, convolution, data filtering, reconstruction of noisy data).

Readings

I. Kuščer, A. Kodre: Matematične metode v fiziki in tehniki, DMFA, Ljubljana 1994.
S. Širca, M. Horvat, Računske metode za fizike, DMFA, Ljubljana 2011.
S. Širca, M. Horvat, Computational Methods for Physicists, Springer, Berlin 2012.
W.H. Press, B.P.Flannery, S.A.Teukolsky, W.T.Vetterling: Numerical Recipes, Third Edition, Cambridge University Press, Cambridge 2007.
J.W. Demmel: Uporabna numerična linearna algebra, DMFA, Ljubljana 2000.
M.H.Kalos, P.A.Whitlock: Monte Carlo methods.

Objectives and competences

Acquaintance with basic model approaches and the ability to handle the basic tools of mathematical modeling. Each weekly unit is a combination of modeling and mathematical tools.

Intended learning outcomes

Knowledge and understanding:

Knowledge of basic model analysis approaches and procedures, understanding the effects end efficacies of individual modeling tools.

Application:

The ability to model data and use procedures of stochastic analysis.

Reflection:

Understanding of the relationship between a physical phenomenon and its model, the reflection of complexities.

Transferable skills:

Presentation of data and the results of their model analysis; more complex data visualisations. Ability to handle computer algorithms with large time and memory requirements.

Learning and teaching methods

Lectures, exercises, homework, consultations.

Assessment

A total score of weekly projects, plus the score of the final project
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

Lecturer's references

ŠIRCA, Simon, HORVAT, Martin. Computational methods for physicists : compendium for students, (Graduate texts in physics). Berlin; Dordrecht: Springer, 2012.

ŠIRCA, Simon, HORVAT, Martin. Računske metode za fizike, DMFA - založništvo, 2011

Jefferson Lab PVDIS Collaboration, MIHOVILOVIČ, Miha, ŠIRCA, Simon, et al. Measurement of parity violation in electron-quark scattering. Nature, 2014, vol. 506, str. 67-70.

SUBEDI, R., POTOKAR, Milan, ŠIRCA, Simon, et al. Probing cold dense nuclear matter. Science, 2008, vol. 320, str. 1476-1478.

GAYOU, O., ŠIRCA, Simon, et al. Measurement of G(Ep)/G(Mp) in (e)over-right-arrowp -> e(p)over-right-arrow to Q(2)=5.6 GeV2. Physical review letters, 2002, vol. 88, str. 092301-1-092301-5.