CoDaDri: Difference between revisions
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domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate | 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 | models to describe these systems and being able to make quantitative predictions from those models | ||
is extremely challenging. | is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems. | ||
== Course description == | == Course description == | ||
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== The Team == | == The Team == | ||
* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] ( | * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational Physics) | ||
* [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data Driven Physics) | * [http://www.phys.ens.fr/~monasson/ Rémi Monasson] (Data Driven Physics) | ||
* [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials) | * [http://www.lps.ens.fr/~cocco/ Simona Cocco] & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials) | ||
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For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. <br> | For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. <br> | ||
The | The Collaboratory 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 == | == Grading == | ||
Revision as of 18:12, 29 August 2021
Computational and Data Driven Physics
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.
Course description
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 & Michel Ferrero (Tutorials)
Where and When
- Lectures on Fridays: 14:00-16:00
- Tutorials on Fridays: 16:00-18:00
- ENS, 29 rue D'Ulm, salle Borel + Djebar
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 Collaboratory 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
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Schedule
Friday, September 3, 2020
- Lecture 1 Introduction to Monte Carlo
- Tutorial 1 Markov Matrix
Friday, September 10, 2020
- Tutorial 2 Markov matrix problems
- Tutorial 3 Thumb rule problems
Homework: Download
Friday, September 17, 2020
- Lecture 2 Basic Sampling
- Lecture 3: Errors and Precision
Friday, September 24, 2020
- Lecture 4: Ising model and phase transitions
- Tutorial 4: Ising model and phase transitions problems
Friday, October 1, 2020
Lecture 6: Importance sampling
Tutorial 6: Faster than the clock algorithms problems
Homework 2: Download
Friday, October 5, 2020
Lecture 7: Optimization & Dijkstra algorithm
Tutorial 7: Simulated annealing problems