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 Scheda Riassuntiva
 Anno Accademico 2017/2018 Scuola Scuola di Ingegneria Industriale e dell'Informazione Insegnamento 098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES Docente Miglio Edie , Vantini Simone Cfu 10.00 Tipo insegnamento Corso Integrato

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing - Civ (Mag.)(ord. 270) - MI (489) INGEGNERIA PER L'AMBIENTE E IL TERRITORIO - ENVIRONMENTAL AND LAND PLANNING ENGINEERING*AZZZZ098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA*AZZZZ098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES

 Programma dettagliato e risultati di apprendimento attesi

# I Part of the course: Numerical methods

## Objectives

The first part of the course will give the students the basic knowledge concerning the most important mathematical and numerical methods used to solve the equation of motion of fluids in porous media and to solve the elastodynamic problem.

## Program

• Introduction to flow in porous media. Numerical methods for single phase flow in porous media: primal (pressure) and mixed formulation. Finite volumes, mixed finite elements and mimetic finite differencing.

• Elastodynamics. Methods for second order hyperbolic equations (Newmark, leap-frog schemes); finite difference and finite element for wave propagation.

• Poroelasticity. Biot equations: Splitting strategy for the coupled problem of flow and structure.

## Course organization

The course is organized in theoretical classes and laboratories.

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# II Part of the course: Statistical methods

## Objectives.

The course aims at providing students with statistical tools for the analysis of data typically encountered in geo-science and environmental applications. The course is organized in theoretical classes and laboratories. Basic knowledge in probability and statistics at bachelor level is suggested.

## Program.

• Euclidean Multivariate Data (brief review). The Euclidean geometry in the real space, Principal Component Analysis, clustering, permutational one- and two-population tests.

• Compositional Data (e.g., chemical compounds, mineralogical compositions, atmospheric pollutants). The Aitchinson geometry in the simplex, transformations of compositional data, Principal Component Analysis of compositional data, clustering of compositional data, permutational one- and two-population tests for compositional data.

• Directional Data (e.g, winds, waves, geological fault directions).The geodesic distance, Principal Component Analysis of directional data, clustering of directional data, permutational one- and two-population tests for directional data.

• Tensor Data (e.g., diffusion of oil, water, vehicles and people).Distances between tensors, clustering of tensor data, permutational one- and two-population tests for tensor data.

• Network Data (e.g., river networks, oil and gas pipelines, mobility networks).Network representations (adjacency, Laplacian, and modularity matrix), distances between networks, clustering of network data, permutational one- and two-population tests for network data.

• Data with Spatial and/or Temporal Dependence. Measures of spatial dependence, covariogram and variogram, estimation and prediction via ordinary and universal Kriging, hidden Markov random fields.

Course organization

The course is made of theoretical lectures (24 hours) followed by lab sessions (16 hours). During the theoretical lectures methods and algorithms will be presented in the proper mathematical framework. During the lab sessions methods and algorithms will be instead illustrated and tested through applications to real data sets. The analyses performed during the lab sessions will be carried out by means the opensource software R (www.r-project.org). Along the course, students are expected to work on a data analysis team project.

 Note Sulla Modalità di valutazione
 The exam consists of a written or oral test concerning both the numerical and statistical part of the course and of a project.

 Bibliografia
 Chen, Huan, Ma, Computational Methods for Multiphase Flows in Porous Media Beirao da Veiga, Lipnikov, Manzini, The Mimetic Finite Difference Method for Elliptic Problems Moczo, Kristek, Galis, The Finite-Difference Modelling of Earthquake Motions: Waves and Ruptures Quarteroni, Numerical models for differential problems V. Pawlowsky-Glahn and A. Buccianti, Compositional data analysis. Theory and application K. V. Mardia and P. Jupp, Directional Statistics (2nd edition) P. I. Good, Permutation, Parametric and Bootstrap Tests of Hypotheses N. Cressie, Statistics for Spatial data E. D. Kolaczyk, Statistical Analysis of Network Data

 Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
64.0
esercitazione
36.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

 Informazioni in lingua inglese a supporto dell'internazionalizzazione
 Insegnamento erogato in lingua Inglese Disponibilità di materiale didattico/slides in lingua inglese Disponibilità di libri di testo/bibliografia in lingua inglese Possibilità di sostenere l'esame in lingua inglese Disponibilità di supporto didattico in lingua inglese
 schedaincarico v. 1.6.6 / 1.6.6 Area Servizi ICT 24/07/2021