2024/2025

Degree | Type | Year |
---|---|---|

4313136 Modelling for Science and Engineering | OT | 0 |

- Name:
- Vicente Mendez Lopez
- Email:
- vicenc.mendez@uab.cat

- Vicente Mendez Lopez
- Pedro Puig Casado
- Alvaro Corral Cano
- Daniel Campos Moreno

You can view this information at the end of this document.

Calculus of several variables. Ordinary and partial differential equations. Introduction to probability theory

The main goal of this course is to provide powerfull tools to deal with the analysis and numerical simulations of stochastic processes both for systems affected by external noise of by internal noise. Applications to ecological and biological systems will be discussed in detail.

- Apply logical/mathematical thinking: the analytic process that involves moving from general principles to particular cases, and the synthetic process that derives a general rule from different examples.
- Apply specific methodologies, techniques and resources to conduct research and produce innovative results in the area of specialisation.
- Apply techniques for solving mathematical models and their real implementation problems.
- Conceive and design efficient solutions, applying computational techniques in order to solve mathematical models of complex systems.
- Formulate, analyse and validate mathematical models of practical problems in different fields.
- Isolate the main difficulty in a complex problem from other, less important issues.

- Apply logical/mathematical thinking: the analytic process that involves moving from general principles to particular cases, and the synthetic process that derives a general rule from different examples.
- Apply specific methodologies, techniques and resources to conduct research and produce innovative results in the area of specialisation.
- Apply stochastic process techniques to predict the behaviour of certain phenomena.
- Apply stochastic process techniques to study models associated with practical problems.
- Identify real phenomena as models of stochastic processes and extract new information from this to interpret reality.
- Implement the proposed solutions reliably and efficiently.
- Isolate the main difficulty in a complex problem from other, less important issues.
- Use specific software to model stochastic processes and, depending on the situation, estimate the corresponding parameters.

First Part:

- Elementary probability
- Stochastic processes. Noise and Markov processes
- Microscopic description: Stochastic differential equations and their integration. Applications to population dynamics

Second Part:

- Mesoscopic description: Master equation. One-step processes. Diffusion approach. Biological and physical examples.
- Random Walks. CTRW. Anomalous diffsuion, Lévy flights and First passage-time problems. Ecological and social applications

Third Part:

- Discrete-time stochastic processes. Continuous state space models: AR, MA, ARMA and ARIMA. Parameter estimation, diagnostic tests and forecasting
- Introduction to discrete state space models: INAR(1) and PoINAR(1).

Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|

Type: Directed | |||

Classroom sessions | 38 | 1.52 | 1, 2, 3, 4 |

Type: Supervised | |||

Mentoring | 9 | 0.36 | 5, 6 |

Type: Autonomous | |||

Personal work | 30 | 1.2 | 2, 3, 5 |

Preparation of assignments | 20 | 0.8 | 1, 2, 4, 6, 8 |

The methodology of the course will combine traditional lectures in the classroom with autonomous activities by the student to practice the concepts seen in the course.

As a novelty, this year the first and the second part of the course (theory and practical applications) will be done intertwined to facilitate the assimilation of the contents.

**Annotation**: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|

Exam of the practical part | 20% | 1 | 0.04 | 1, 3, 4, 8 |

Exam of the theoretical part | 25% | 2 | 0.08 | 1, 2, 3, 4, 5, 6 |

Simulations and practical works | 55% | 50 | 2 | 1, 2, 3, 4, 5, 6, 7, 8 |

The avaluation will be divided into the three parts of the course**:**

**First part. (33.3%)**

There will be a problems assignment (10% of the final mark) and a general exam of this part (23.3%)

**Second part. (35%)**

There will be a short project to work with the concepts and techinques seen in the classroom (15% of the final mark) and a short exam of this part (18.3%).

**Third part (30%)**

There will be 2 or 3 assignments

Basic:

**V. Méndez, D. Campos, F. Bartumeus.****Stochastic Foundations in Movement Ecology,****Springer-Verlag****,****2014****C.W. Gardiner,***Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences*. Springer. Berlin. 1990**L.J.S. Allen,***An Introduction to Stochastic Processes with Applications to Biology*. Chapman & Hall/CRC, Boca Ratón. 2011**R. Toral, P. Colet.***Stochastic Numerical Methods*. Wiley-VCH, 2014

Complementary:

**N. van Kampen,***Stochastic Processes in Physics and Chemistry*, Third Edition (North-Holland Personal Library) 2007**J. Rudnick and G. Gaspari.***Elements of the Random Walk*. Cambridge Univ. Press, 2004**N.C. Petroni.***Probability and Stochastic Processes for Physicists*. Springer-Verlag, 2020**N. Lanchier.***Stochastic Modelling*. Springer-Verlag, 2017

The practical activities of the course will be carried out using Python and R languages

Name | Group | Language | Semester | Turn |
---|---|---|---|---|

(TEm) Theory (master) | 1 | English | first semester | afternoon |