# CoDaDri2

This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.

## Breaking news

Example of theory exercises of past years for the January final examination:

## Course description

Modern physics is characterized by an increasing complexity of systems under investigation, in domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate models to describe these systems and being able to make quantitative predictions from those models is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes.

Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications borrowed from various domains of physics. We will focus on methods and algorithms and physics, not on programming and heavy numerics! You will have to hand in 3 homeworks.

## The Team

- Alberto Rosso (Computational physics)
- Rémi Monasson (Data-driven physics)
- Simona Cocco & Jorge Fernandez de Cossio Diaz & Michel Ferrero (Tutorials)
- Vincenzo Maria Schimmenti (Tutor)

## Where and When

- Lectures on Fridays: 14:00-16:00
- Tutorials on Fridays: 16:00-18:00
- JUSSIEU salle 24.34.201.

Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.

## Slack

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the Computational and Data Driven Physics Slack. In order to join the Slack use the following invitation link.

## Computer Requirements

**No previous experience in programming is required.**

**Programming Language: Python**

For practical installation, we recommand either to use Anaconda (See Memento Python) or use google colab.

The Colab platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.

## Grading

**Computational Physics:**

- Homework 1: 5 points
- Homework 2: 5 points
- Multiple Choice Questions: 10 points

**Data Driven Physics:**

- Homework 3: 5 points
- Final exam in January: 15 points

## Schedule Computational Physics

**Friday, September 2, 2022 **

- Lecture 1 Introduction to Monte Carlo

- Tutorial 1 Markov matrices (solutions)

- Homework 1 (deadline September 23)

- Introductory notebooks: python, numpy and matplotlib

**Friday, September 9, 2022**

- Lecture 2 Basic Sampling

- Tutorial 2 Thumb rule (solutions)

**Friday, September 16, 2022**

- Lecture 3 : Importance sampling

- Tutorial 3: Faster than the clock algorithms (solutions)

**Friday, September 23, 2022**

- Lecture 4: Ising model and phase transitions

- Tutorial 4: Ising model and phase transitions (solutions)

- Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!

- Homework 2 (deadline October 21)

**Friday, September 30, 2022**

- Lecture 5: Optimization & Dijkstra algorithm

- Tutorial 5: Simulated annealing (solutions)

**Friday, October 7, 2022**

- Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.

- Solutions: You can find them here. The correct answers are marked in bold.

## Schedule Data-driven Physics

Book on "From Statistical Physics to Data-Driven Modelling" by S. Cocco, R. Monasson, F. Zamponi File

**Friday, October 14, 2022**

- Lecture 7: Introduction to Bayesian inference. Extra material: Combinatorial identities by Hohle & Held 2006

- Tutorial 7: Bayesian inference and single-particle tracking. Questions.

Google Colab Starting Notebook.Google Collab Solutions.Solutions.

**Friday, October 21, 2022**

- Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [3]

- Tutorial 8:Analysis of quantum trajectories of atoms in a cavity to infer the numbers of photons in the cavity. Questions. Google Collab Starting notebook.Bibliography SolutionsGoogle Collab Solutions.

- Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!

**Friday, October 28, 2022**

- Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [4]

Starting Notebook on Google Colab. Tutorial Solution on Google Colab.

Homework 3 Starting Notebook on Google Colab. (Send your copy of Homework 3 to numphys.icfp at gmail.com by December 1). Homework 3 Solutions on Google Colab. Solution comments

**Friday, November 25, 2022**

- Lecture 10: Priors, regularisation, sparsity

- Tutorial 10:

Bayesian Inference and Priors for the analysis of gravitational waves (Notes). Biblio Starting notebook on artificial data. Notebook on real data Solutions (Artificial data)

**Friday, December 2, 2022**

- Lecture 11: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [5]
- Tutorial 11: Hidden Markov Models. Questions Starting notebook

**Friday, December 9, 2021**

- Lecture 12: Probabilistic graphical models

**Friday, December 16, 2022**

Tutorial 13: Inferring structural contacts from protein sequences Notes Start Notebook Solutions

**Friday, January 06, 2023**

EXAM.

Theoretical exercises Practical notebook (Google Colab version) Practical notebook & data (Local notebook version)

For the WiFi:

- Sélectionner "WIFI-GUEST" - Mot de passe : PhysiqueENS - Ouverture automatique d'une page web dans le navigateur (par défaut), - Saisir le "Super secret password" : exam.m2.icfp@phys.ens.fr (Ce n'est pas une vraie adresse)