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* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo | * [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] | * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix | ||
'''Friday, September 11, 2020''' | '''Friday, September 11, 2020''' | ||
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Lecture 2: Basic Sampling | Lecture 2: Basic Sampling | ||
Tutorial 2: Markov matrix | [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2:] Markov matrix | ||
Revision as of 17:28, 10 September 2020
Numerical Physics and Machine Learning
Course description
We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems. We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.
The Team
- Alberto Rosso (Numerical Physics)
- Florent Krzakala (Machine Learning)
- Marko Medenjak (Tutorials)
Where and When
- Lectures on Fridays: 14:00-16:00
- Tutorials on Fridays: 16:00-18:00
- ENS, 24 rue Lhomond, room Conf IV (2nd floor)
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 (10 points each) + 1 MCQ (20 points)
Schedule
Friday, September 4, 2020
- Lecture 1 Introduction to Monte Carlo
- Tutorial 1 Markov Matrix
Friday, September 11, 2020
Zoom link [1]
Lecture 2: Basic Sampling
Tutorial 2: Markov matrix
Friday, September 18, 2020
Lecture 2: Error evaluation
Tutorial 2: Thumb rule
Friday, September 25, 2020
Lecture 3: Ising model and phase transitions
Tutorial 3: Ising model and phase transitions
Due: Homework 1
Friday, October 2, 2020
Lecture 3: Quantum particle
Tutorial 3: Time evolution (quantum)
Homework 2:
Friday, October 9, 2020
Lecture 4: Importance sampling
Tutorial 4: Faster than the clock algorithms
Friday, October 16, 2020
Lecture 5: Optimization & Dijkstra algorithm
Tutorial 5: Simulated annealing
Due: Homework 2
Friday, October 23, 2020
QCM: 2 hours, 20 questions for 20 points
Lecture 5: more on ptimization
References
- SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)
- Other references are specified in each lectures