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* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computationa Physics) | * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computationa Physics) | ||
* [https://florentkrzakala.com/ Remy Monasson] (Data Driven Physics) | * [https://florentkrzakala.com/ Remy Monasson] (Data Driven Physics) | ||
* Simona Cocco & | * Simona Cocco & Michel Ferrero (Tutorials) | ||
== Where and When == | == Where and When == | ||
| Line 30: | Line 30: | ||
* ENS, 29 rue D'Ulm, salle Borel + Djebar | * ENS, 29 rue D'Ulm, salle Borel + Djebar | ||
== Computer Requirements == | |||
'''No previous experience in programming is required.''' <br> | |||
'''Programming Language: Python''' | |||
For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. <br> | |||
The Colaboratory 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 == | |||
Homeworks (50% of the mark) + 1 MCQ (50% of the final mark) | |||
== Schedule == | |||
''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project] | |||
'''Friday, September 4, 2020 ''' | |||
* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo | |||
* [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix | |||
'''Friday, September 11, 2020''' | |||
* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling | |||
* [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems] | |||
'''Friday, September 18, 2020''' | |||
* [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision | |||
* [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems] | |||
Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download] | |||
'''Friday, September 25, 2020''' | |||
* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions | |||
* [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] | |||
'''Friday, October 2, 2020''' | |||
[https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle | |||
[https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems] | |||
Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download] | |||
'''Friday, October 9, 2020''' | |||
[https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling | |||
[https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems] | |||
''' Friday, October 16, 2020''' | |||
'''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP) | |||
[https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization & Dijkstra algorithm | |||
[https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems] | |||
Revision as of 14:25, 2 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.
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 (Computationa Physics)
- Remy 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 Colaboratory 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
Homeworks (50% of the mark) + 1 MCQ (50% of the final mark)
Schedule
Project in Markox Chain Monte Carlo Manon Michel's Project
Friday, September 4, 2020
- Lecture 1 Introduction to Monte Carlo
- Tutorial 1 Markov Matrix
Friday, September 11, 2020
- Lecture 2 Basic Sampling
- Tutorial 2 Markov matrix problems
Friday, September 18, 2020
- Lecture 3: Errors and Precision
- Tutorial 3 Thumb rule problems
Homework: Download
Friday, September 25, 2020
- Lecture 4: Ising model and phase transitions
- Tutorial 4: Ising model and phase transitions problems
Friday, October 2, 2020
Lecture 5: Quantum particle
Tutorial 5: Time evolution (quantum) problems
Homework 2: Download
Friday, October 9, 2020
Lecture 6: Importance sampling
Tutorial 6: Faster than the clock algorithms problems
Friday, October 16, 2020
GoToMeeting link [1] (Room 1 M2 ICFP)
Lecture 7: Optimization & Dijkstra algorithm
Tutorial 7: Simulated annealing problems