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This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course. | |||
= | == Breaking news == | ||
Example of theory exercises of past years for the January final examination: | |||
* [https://drive.google.com/file/d/1zlqXeb9L0l39G85E3iTVwXOCOlq-FnR1/view?usp=sharing] Example 1: hypothesis testing | |||
* [https://drive.google.com/file/d/13KrLvNXwpKb3vB-iUwzWxGsbJbH4vpHt/view?usp=sharing] Example 2: distribution of log-likelihoods | |||
<!-- * You can find the solutions of the MCQ [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]. The correct answers are marked in bold. --> | |||
== Course description == | |||
Modern physics is characterized by an increasing complexity of systems under investigation, in | Modern physics is characterized by an increasing complexity of systems under investigation, in | ||
| Line 10: | Line 17: | ||
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. The goal of the course is to provide the tools and concepts necessary to tackle those systems. | is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems. | ||
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. | 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. | ||
| Line 21: | Line 26: | ||
on programming and heavy numerics! You will have to hand in 3 homeworks. | on programming and heavy numerics! You will have to hand in 3 homeworks. | ||
*[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] | <!-- *[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] --> | ||
== The Team == | == The Team == | ||
| Line 27: | Line 32: | ||
* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Computational physics) | * [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] & Jorge Fernandez de Cossio Diaz & [https://www.cpht.polytechnique.fr/?q=en/node/110 Michel Ferrero] (Tutorials) | ||
* [ Vincenzo Maria Schimmenti] (Tutor) | * [https://github.com/Schimmenti Vincenzo Maria Schimmenti] (Tutor) | ||
== Where and When == | == Where and When == | ||
| Line 34: | Line 39: | ||
* Lectures on Fridays: 14:00-16:00 | * Lectures on Fridays: 14:00-16:00 | ||
* Tutorials on Fridays: 16:00-18:00 | * Tutorials on Fridays: 16:00-18:00 | ||
* | * JUSSIEU salle 24.34.201. | ||
Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus. | |||
== Slack == | == 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 | If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the | ||
[ | [https://computational-prb2883.slack.com/ssb/redirect Computational and Data Driven Physics Slack]. In order to join the Slack | ||
use the following [ | use the following [https://join.slack.com/t/computational-prb2883/shared_invite/zt-1fbx1i96a-CRDyBiOr~cn0LCdJZv~fEw invitation link]. | ||
== Computer Requirements == | == Computer Requirements == | ||
| Line 49: | Line 55: | ||
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 [https://colab.research.google.com Colab 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 == | ||
| Line 56: | Line 62: | ||
* Homework 1: 5 points | * Homework 1: 5 points | ||
* Homework 2: 5 points | * Homework 2: 5 points | ||
* Multiple Choice Questions | * Multiple Choice Questions: 10 points | ||
'''Data Driven Physics:''' | '''Data Driven Physics:''' | ||
| Line 66: | Line 68: | ||
* Final exam in January: 15 points | * Final exam in January: 15 points | ||
== Schedule == | == Schedule Computational Physics == | ||
| Line 73: | Line 75: | ||
* [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/ | * [https://colab.research.google.com/drive/1gf7oRQIpvSvMScyJa5hexB9CCZvdozPs#scrollTo=lDznlLouvlFx Tutorial 1] Markov matrices ([https://colab.research.google.com/drive/1x9iEHG_F522uFCvD_jGdvHaMETAHfO6-#scrollTo=eSu-nLZ6vm8X solutions]) | ||
* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline September 23) | |||
* Introductory notebooks: [https://colab.research.google.com/drive/1Ovrh1JjLzMMnxtasDgR1j84CHGU6hMxA python], [https://colab.research.google.com/drive/14PWu-C171pYhV8ven-Txc4wz5Tv1DCsK numpy] and [https://colab.research.google.com/drive/1fPjwrdxQPSyfXniyAHmyOcF_gK2fqjts matplotlib] | |||
'''Friday, September 9, 2022''' | '''Friday, September 9, 2022''' | ||
* | * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling | ||
* [https://colab.research.google.com/drive/ | * [https://colab.research.google.com/drive/1C93cApFZNJKQXi9YiBdKkXNBus88iZ9z#scrollTo=FEQYvp4cfvS0 Averages and error bars] | ||
* [https://colab.research.google.com/drive/ | * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 2] Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) | ||
'''Friday, September 16, 2022''' | '''Friday, September 16, 2022''' | ||
* [https://drive.google.com/file/d/ | * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 3 ]: Importance sampling | ||
* | * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 3]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions]) | ||
| Line 104: | Line 104: | ||
* [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]) | ||
* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks! | |||
* | |||
* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 21) | |||
| Line 114: | Line 114: | ||
* [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]) | ||
'''Friday, October 7, 2022''' | '''Friday, October 7, 2022''' | ||
* Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points. | |||
* [https:// | * Solutions: You can find them [https://colab.research.google.com/drive/16rPvG4ifcs2MMsaKYb9sxVlNbm-4JQGD?usp=sharing here]. The correct answers are marked in bold. | ||
== Schedule Data-driven Physics == | |||
Book on "From Statistical Physics to Data-Driven Modelling" by S. Cocco, R. Monasson, F. Zamponi [https://drive.google.com/file/d/1_I1MML-Le8SlT-NF6XJB5Jm5MD5FW0Xz/view?usp=sharing File] | |||
'''Friday, October 14, 2022''' | |||
* Lecture | * Lecture 7: Introduction to Bayesian inference. Extra material: [https://drive.google.com/file/d/17VA5XWZ07aqWcvjQez64VqctAeWsJe69/view?usp=sharing Combinatorial identities by Hohle & Held 2006] | ||
* Tutorial | * Tutorial 7: Bayesian inference and single-particle tracking. [https://drive.google.com/file/d/1SeT5S0gS5MFFStwccRQ1R5v9uuk8Qqq4/view?usp=sharing Questions]. | ||
[https://colab.research.google.com/drive/1DNHVysMQLdseIbgAEW_fgTXgngFqMg9-?usp=sharing Google Colab Starting Notebook].[https://colab.research.google.com/drive/1Wo4pkDfvzads4CNR2HibyeRTpIRYUqoq?usp=sharing Google Collab Solutions].[https://drive.google.com/file/d/1Q9IjVUeL7AA8uWk8DX9Vy9vuUQ21RYh2/view?usp=sharing Solutions]. | |||
<!--[Notebook [https://drive.google.com/file/d/1ZJmpdaTQVUttEaaI1ch-reFfZGI93454/view?usp=sharing]-- [https://drive.google.com/file/d/1Ba_kRo3_XMIaDLNYZlbZEw-wGXKS-29I/view?usp=sharing Starting Notebook]. --> | |||
'''Friday, October 21, 2022''' | |||
'''Friday, October | |||
* 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] | ||
* Tutorial 8: [https://drive.google.com/file/d/ | * Tutorial 8:Analysis of quantum trajectories of atoms in a cavity to infer the numbers of photons in the cavity. [https://drive.google.com/file/d/103-vCGVnY7QNUUo08f6mv6UlGmK85iZ6/view?usp=sharing Questions]. [https://drive.google.com/file/d/14bsrCZQVLY-vMqKAH8AFo7F2A1Jsz0Se/view?usp=sharing Google Collab Starting notebook].[https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1-jJBjR8wv2pfANoiWKXltEP0R1n9ZP1g/view?usp=sharing Solutions][https://colab.research.google.com/drive/1Wk0uH9UbHi3HkABVjBlzeTDFcncolVLN?usp=sharing=sharing Google Collab Solutions]. | ||
<!--[https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook] --> | |||
* Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks! | * Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks! | ||
'''Friday, October | '''Friday, October 28, 2022''' | ||
* Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing] | * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing] | ||
* Tutorial 9: [https://drive.google.com/file/d/ | * Tutorial 9: Replay of the neuronal activity during sleep after a task [https://drive.google.com/file/d/112_PGk57Ateg8UxHhS40maeZj5cNUn8G/view?usp=sharing Questions]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio]. | ||
[https://drive.google.com/file/d/ | <!-- [https://drive.google.com/file/d/11R21V9kJnS6Igfdc-ZmbOs-uVw4xAtH2/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook][https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook] --> | ||
[https://colab.research.google.com/drive/1xxuQjJAYPJLXVqUXTm75dFdyn0vDxEzj?usp=sharing Starting Notebook on Google Colab]. | |||
[https://colab.research.google.com/drive/139ETiVJa8ZHvjwPcnv1aieK9buM00zt3?usp=sharing Tutorial Solution on Google Colab]. | |||
[https://drive.google.com/file/d/18CrQkbu_8rMUz84pzCFSHP4sNybuY0NF/view?usp=sharing Homework 3 Starting Notebook on Google Colab]. | |||
(Send your copy of Homework 3 to numphys.icfp at gmail.com by December 1). | |||
[https://colab.research.google.com/drive/1W5V_-6jPZIQsnZH9Lk4aFZryQ6z7dgyN?usp=sharing Homework 3 Solutions on Google Colab]. | |||
[https://docs.google.com/document/d/1a8IuIGceWMR_PvidwCxpMGsHNvEyOV-H/edit?usp=sharing&ouid=117098638067589756688&rtpof=true&sd=true Solution comments] | |||
'''Friday, November 25, 2022''' | |||
* Lecture 10: Priors, regularisation, sparsity | |||
* Tutorial 10: | |||
[https://drive.google.com/file/d/1J5WTb1SvKZz3Px_nbtKYkFBQYwmSWF54/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves (Notes)]. | |||
[https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio] | |||
[https://colab.research.google.com/drive/1e9q4QKgulmMLvcdAAWOdTX37ygNuJ2Hi?usp=sharing Starting notebook on artificial data]. | |||
[https://colab.research.google.com/drive/1ibEYkzAaa_nJqkmd2XUlrcA__-op-Z5E?usp=sharing Notebook on real data] | |||
[https://colab.research.google.com/drive/1v651xLgcEwjqjfDHwwPqiIwwuxf00iI3?usp=sharing Solutions (Artificial data)] | |||
<!--[https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ] --> | |||
'''Friday, December 2, 2022''' | |||
* Lecture 11: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ] | |||
* Tutorial 11: Hidden Markov Models. [https://drive.google.com/file/d/1OIl7NFWtz8zR_OHkhX9vGvyKrxzHz9Do/view?usp=share_link Questions] [https://colab.research.google.com/drive/19iu2kCJi_JWej9u5xYFl1xlawt6YAT9n#scrollTo=MBc6-68_4p-8 Starting notebook] | |||
[https://colab.research.google.com/drive/1EVCN6av94zSFzRF7lvVLLNNw6XRgP1nU?usp=sharing Solutions] | |||
<!-- | |||
Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] <!--[https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook] | |||
--> | |||
'''Friday, December 9, 2021''' | |||
* | * Lecture 12: Probabilistic graphical models | ||
<!-- | |||
* Tutorial 12: | |||
[https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data] | |||
--> | |||
<!--[https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions] --> | |||
'''Friday, December | '''Friday, December 16, 2022''' | ||
Tutorial 13: Inferring structural contacts from protein sequences | |||
[https://drive.google.com/file/d/1a5uOdBkZpkGkWYtekW9_EhJljwIvgkYG/view?usp=sharing Notes] | |||
[https://colab.research.google.com/drive/1AKuUPY9l_ENizcijO0V0dncaLGLUAMXH?usp=sharing Start Notebook] | |||
[https://colab.research.google.com/drive/1QRt6xLnhE6z1R_M5Pcil99NNccJt2CxB?usp=sharing Solutions] | |||
'''Friday, January 06, 2023''' | |||
EXAM. | |||
[https://drive.google.com/file/d/1mhf9nMa-MzkwdOlGGLlsp_Miu_Ot1i0N/view?usp=sharing Theoretical exercises] | |||
[https://colab.research.google.com/drive/1GwP-j7zmS8E74QPGmFoZAyWJB3c9Pl56#scrollTo=f4YJsCKC3h7N Practical notebook (Google Colab version)] | |||
[https://drive.google.com/file/d/1mx_kqt7-9ekYIlS2zhkEY41nesHan7BB/view?usp=sharing Practical notebook & data (Local notebook version)] | |||
For the WiFi: | |||
- Sélectionner "WIFI-GUEST" | |||
- Mot de passe : PhysiqueENS | |||
- Ouverture automatique d'une page web dans le navigateur (par défaut), | |||
- Saisir le "Super secret password" : exam.m2.icfp@phys.ens.fr (Ce n'est pas une vraie adresse) | |||
<!-- | |||
* Lecture 13: Unsupervised learning and representations | * Lecture 13: Unsupervised learning and representations | ||
* | * Tutorial 13: How restricted Boltzmann machines learn | ||
--> | |||
'''Final examination of the data-driven course (January 7, 2022)''' | <!-- '''Final examination of the data-driven course (January 7, 2022)''' | ||
* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing] | * Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing] | ||
| Line 212: | Line 230: | ||
* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing] | * On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing] | ||
* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] | * Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] --> | ||
Latest revision as of 14:55, 6 January 2023
This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.
Breaking news
Example of theory exercises of past years for the January final examination:
Course description
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.
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 & Jorge Fernandez de Cossio Diaz & Michel Ferrero (Tutorials)
- Vincenzo Maria Schimmenti (Tutor)
Where and When
- Lectures on Fridays: 14:00-16:00
- Tutorials on Fridays: 16:00-18:00
- JUSSIEU salle 24.34.201.
Don't be scared by the long number: it means that our new room is located in the corridor on the second floor, between tower 24 and tower 34 of Jussieu campus.
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 Colab 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: 10 points
Data Driven Physics:
- Homework 3: 5 points
- Final exam in January: 15 points
Schedule Computational Physics
Friday, September 2, 2022
- Lecture 1 Introduction to Monte Carlo
- Tutorial 1 Markov matrices (solutions)
- Homework 1 (deadline September 23)
- Introductory notebooks: python, numpy and matplotlib
Friday, September 9, 2022
- Lecture 2 Basic Sampling
- Tutorial 2 Thumb rule (solutions)
Friday, September 16, 2022
- Lecture 3 : Importance sampling
- Tutorial 3: Faster than the clock algorithms (solutions)
Friday, September 23, 2022
- Lecture 4: Ising model and phase transitions
- Tutorial 4: Ising model and phase transitions (solutions)
- Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!
- Homework 2 (deadline October 21)
Friday, September 30, 2022
- Lecture 5: Optimization & Dijkstra algorithm
- Tutorial 5: Simulated annealing (solutions)
Friday, October 7, 2022
- Test: Multiple Choice Questions. For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.
- Solutions: You can find them here. The correct answers are marked in bold.
Schedule Data-driven Physics
Book on "From Statistical Physics to Data-Driven Modelling" by S. Cocco, R. Monasson, F. Zamponi File
Friday, October 14, 2022
- Lecture 7: Introduction to Bayesian inference. Extra material: Combinatorial identities by Hohle & Held 2006
- Tutorial 7: Bayesian inference and single-particle tracking. Questions.
Google Colab Starting Notebook.Google Collab Solutions.Solutions.
Friday, October 21, 2022
- Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [3]
- Tutorial 8:Analysis of quantum trajectories of atoms in a cavity to infer the numbers of photons in the cavity. Questions. Google Collab Starting notebook.Bibliography SolutionsGoogle Collab Solutions.
- Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!
Friday, October 28, 2022
- Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [4]
Starting Notebook on Google Colab. Tutorial Solution on Google Colab.
Homework 3 Starting Notebook on Google Colab. (Send your copy of Homework 3 to numphys.icfp at gmail.com by December 1). Homework 3 Solutions on Google Colab. Solution comments
Friday, November 25, 2022
- Lecture 10: Priors, regularisation, sparsity
- Tutorial 10:
Bayesian Inference and Priors for the analysis of gravitational waves (Notes). Biblio Starting notebook on artificial data. Notebook on real data Solutions (Artificial data)
Friday, December 2, 2022
- Lecture 11: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [5]
- Tutorial 11: Hidden Markov Models. Questions Starting notebook
Friday, December 9, 2021
- Lecture 12: Probabilistic graphical models
Friday, December 16, 2022
Tutorial 13: Inferring structural contacts from protein sequences Notes Start Notebook Solutions
Friday, January 06, 2023
EXAM.
Theoretical exercises Practical notebook (Google Colab version) Practical notebook & data (Local notebook version)
For the WiFi:
- Sélectionner "WIFI-GUEST" - Mot de passe : PhysiqueENS - Ouverture automatique d'une page web dans le navigateur (par défaut), - Saisir le "Super secret password" : exam.m2.icfp@phys.ens.fr (Ce n'est pas une vraie adresse)