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* Final exam in January: 20 points | * Final exam in January: 20 points | ||
== Schedule == | |||
'''Friday, September 3, 2021 ''' | <!-- '''Friday, September 3, 2021 ''' | ||
* [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 | ||
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* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) | * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) | ||
* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1) | * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1) --> | ||
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'''Friday, September 17, 2021''' | |||
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* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling | * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling | ||
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* [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions | * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions | ||
* [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) | * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --> | ||
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'''Friday, October 1, 2021''' | |||
<!--'''Friday, October 1, 2021''' | |||
* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm | * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm | ||
* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) | * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --> | ||
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* [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems] | * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems] | ||
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* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks! | |||
<!-- * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks! | |||
* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22) | * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22) | ||
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* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling | * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling | ||
* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions]) | * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions]) --> | ||
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'''Friday, October 22, 2021''' | |||
<!-- '''Friday, October 22, 2021''' | |||
* Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing] | * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing] | ||
Latest revision as of 11:34, 2 September 2022
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 ==