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* [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial | * Tutorial 6: Simulated annealing | ||
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* [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 6]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems] | |||
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* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture | * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling | ||
* [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial | * Tutorial 7: Faster than the clock algorithms | ||
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* [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 7]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems] | |||
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Revision as of 16:41, 17 September 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 & David Lacoste & 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
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 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
Computational Physics:
- Homework 1: 5 points
- Homework 2: 5 points
- Multiple Choice Questions in November: 10 points
Data Driven Physics:
- Final exam in January: 20 points
Schedule
Friday, September 3, 2021
- Lecture 1 Introduction to Monte Carlo
- Tutorial 1 Markov Matrix
Friday, September 10, 2021
- Introductory notebooks: python, numpy and matplotlib
- Homework 1 (deadline October 1)
Friday, September 17, 2021
- Lecture 2 Basic Sampling
- Lecture 3: Errors and Precision
Friday, September 24, 2021
- Lecture 4: Ising model and phase transitions
- Tutorial 4 - Ising model and phase transitions
Friday, October 1, 2021
- Lecture 6: Optimization & Dijkstra algorithm
- Tutorial 6: Simulated annealing
- Homework 2
Friday, October 8, 2021
- Lecture 7: Importance sampling
- Tutorial 7: Faster than the clock algorithms
Friday, October 15, 2021
- Lecture 8: Introduction to Bayesian inference
- Tutorial 8:
Friday, October 22, 2021
- Lecture 9: Asymptotic inference and information
- Tutorial 9:
Friday, October 29, 2021
- Lecture 10: High-dimensional inference and Principal Component Analysis
- Tutorial 10:
Friday, November 26, 2021
- Lecture 11: Priors, regularisation, sparsity
- Tutorial 11:
Friday, December 3, 2021
- Lecture 12: Network inference
- Tutorial 12:
Friday, December 10, 2021
- Lecture 13: Supervised learning and phase transitions
- Tutorial 13:
Friday, December 17, 2021
- Lecture 14: Unsupervised learning and representations
- Tutorial 14: