Numerical Modeling, inverse problems
Steady progress but no real revolution ! Unapprochable inverse problems? Robust solutions ?
Computer Science and Applied Mathematics Workshop18 - 22 September 2017
Introduction to research byinnovation
Objectives of this workshop
Scientific Committee : Pascal Monasse, Eric Duceau et Mohammed El Rhabi.Teacher Team : see page 6 Web page : https://imi2017.enpc.fr/
I- Objectives : This creative workshop will allow to initiate our students, second year (Master degree)from Computer Science and Applied Mathematics (IMI) of École des Ponts and studentsfrom Master's Degree in Advanced Physics and Applied Mathematics-University of theBalearic Islands, to "research for innovation". At the end of this week, students willacquire (or confirm) the necessary skills: there are a number of questions that must beaddressed before tackling a project. Ideally, they will be able to propose the first steptowards a solution.
"Real-life" problem are often ill-posed problems. A real challenge for the engineer is toreframe these difficult problems and turn them into a "more tractable one". However,applied mathematics engineers need to engage with all the actors (other engineers orsometimes customers). An opportunity to engage these dialogues: to be able toensure a well-honed ability to explain complex concepts, to understand the problemsof each contact and to suggest the tools that best meet their needs.
17 projects are proposed by researchers or industrials. The students should decide ona topic (you choose a project). Each project is handled by a group of 5-6 students.Deadline: 11 June 2017.
Another interesting viewpoint is the intercultural work. Spanish students (master'sdegree, UIB) will work with French students (master's, École des Ponts).
This workshop is valued at 1.5 credits (ECTS) for our students.
Last, this week may be the opportunity to give rise to vocations.
II-Workshop program:
Two morning sessions will be devoted to conferences or site visits.The rest of the week takes place in a workshop by groups of students: case study in smallgroups (6) on a project of your choice.
IV- Validation :
Active participation in the programme.17 projects were proposed to students (max 6 students per project).
It is expected:
- For each project : A poster
- In the Posters each participant displays the different steps :
- 1 page for the introduction.- 6 pages: describing the work of the group (analysis and main
contributions according to the instructions of the supervisors).- 1 page « conclusion and future work ». Be sure, you don't forget the
bibliography.- Tuesday 19/09, the supervisor gives instructions for the poster.- Students prepare their poster then send their first version to their supervisor
before 2 October 2017. Deadline: 9 October 2017.
The week of October 16, 2017:
Posters sessions : at least one student of each group must present to lay out the workto others students and professors each day from 12h00 to 14h00.
Rating scale : 20 points: 3 principal parts:
1. The supervisor: 10 points: review:The attendance : « -1 » first absence, « -4 » for the second, « 0 »(overall score) ! Participation : 2Pertinence : 4Progression : 4
2. Poster: 6 points: This part will be evaluated by professors/teachers of theSchool (and / or) the directorate of studies (academic directors /managers). They will choose “their” projects:
Quality of the presentation: 2 Scientific and technical content: 2Approach: 2
3. « Oral presentation »: 4 points:
Speech timing: 10'
Structure of the talk
Project control
Practical issues: From 10 October until 13 October 2017:
- B/W printing : Prony wing, 4th floor (computer room).
- Color printing ; Vicat wing, V216, V217 and V219.
- If you need any further information, please contact Ms Mortier (V216).
Schedule
Dates
HorairesMonday18/09/17
Tuesday19/09/17
Mercredi20/09/17
Thursday21/09/17
Friday22/09/17
9H009h15
Workshoppresentation
Visit of Mallorca
StartupWork in groups
(withoutsupervisor)
C3Innovation ?Alon Rozen
9h159h45
C1Research @UIB
Work ingroups
(withsupervis
or) 9H4510h15
C2Research for
innovation?Eric Duceau Oral pitches
(10'+5') mixte Jury :
Ponts+UIB+UCA
(Marrakesh)
10h1512h00
Projectpresentation,
Work in groups(with
supervisor)
12h0013h30
lunch lunch lunch lunchlunch
13h4518h30
Work in groups(with supervisor)
Work in groups(without
supervisor)
Work in groups(withoutsupervisor)
Preparation of Oralpitches
Departure for theairport
Residence : Hostel Fleminghttps://www.booking.com/hotel/es/hostel-fleming.fr.htm
The Team
Conferences Title
C1 Research @ UIB Prof @ UIBC2 Research for innovation ? Eric DuceauC3 Innovation ? Alon Rozen
Projects
Title Supervisors
P1 Machine Learning for Quantum Chemistry Grégoire Ferré and GabrielStoltz
P2 Reduced Basis Methods for Parameterized Partial Differential Equations
Pierre-Loïk Rothé and FrédéricLegoll
P3 Machine learning approach for the recognition of handwritten arabic characters
Saïd Raghay and OumaïmaBanouar
P4 Emergence of collective movements:Application to the birds’ flight
Abdelghani ElmoussaouiAbdelilah Hakim andMohammed El Rhabi
P5 Digitization of documents with a mobile device Idriss El Mourabi, AbdelilahHakim and Pascal Monasse
P6 Blur reduction in a photograph of a document Amine Laghrib
and Pascal Monasse
P7 Blind source separation and ApplicationsAbdelghani Ghazdali,Guillaume Obozinski and Abdelilah Hakim
P8 Deblurring algorithm for licence plate images Amine Laghrib and AbdelilahHakim
P9 Analytical approach for the problem of rigid bodies with simultaneous contacts
Aissam Jebrane, AbdelilahHakim and Pierre Argoul
P10 Audiovisual communication Karima Chelbi
P11 "Creative destruction" in industrial organization Abdelkader Slifi and AlonRozen
P12 Sparse methods for the estimation of variance-covariance and PCA.
Ernesto Palidda, AurélienAlfonsi et Bernard Lapeyre
P13 Blockchain & smart contracts : economic impact Nadia Filali, and Abdelkader Slifi
P14 Blockchain, IoT, securtity, smart city (à confirmer) Louis Grandboulan, and Eric Duceau
P15 Future transportation systems Eric Duceau and Saïd Raghay
P16 Matching of postal addresses Xavier Clerc
P17 Storage Calibration for a solar power plant Henri Gérard
P1 Machine Learning for Quantum Chemistry
Supervisors: Grégoire Ferré and Gabriel [email protected]
DescriptionMany industrial challenges now rely on computing materials’ properties:aircrafts’ wings, radiation damages, resistance of concrete, etc. For complex materials, it is sometimes impossible to come up with a macroscopic model of the considered system (constitutive equation), and in this case one generally uses direct simulation of interacting particles. On the other hand, in the drug sector, it is paramount to understand the behaviour ofmolecules (unfolding, dissociation, etc). In this situation, we often need to resort to molecular dynamics and quantum chemistry to evaluate numerically the properties of a given molecule.For both cases, the computational cost is very high: it is one of the main activitiesof the super computer project (TGCC) of the CEA (Commissariat à l'Energie Atomique). A challenge triggered by these problems is the need for solving the Schrödinger equation for many atomic configurations, which is too long for large-scale applications at the time being.For one decade, Machine Learning has emerged as a path towards a solution [1]. This idea follows that of Statistical Learning in general: from a database (typically made of solutions of the Schrödinger equation), we aim at building (or training) a statistical model of solution. If the principle is clear, it isnot so easy to use in practice, and this for two reasons: the state space (allpossible atomic configurations) is huge, and has physical invariances(translations, rotations, permutations), that have to be taken into account directly in the algorithm.Aim of the projectThe goal of this project is to present these original applications of MachineLearning. We will focus on a kernel method developed in a recent paper [2]. In a first time, students will learn some background both on kernel methods in Machine Learningand on quantum chemistry. Then, they will start using a basic implementation of themethod in Python, before addressing the influence of the parameters on the results and(possibly) implementing new kernels in the program.
Bibliography
[1] Behler, J., & Parrinello, M. (2007). Generalized neural-network representation ofhigh-dimensional potential-energy surfaces. Physical review letters, 98(14), 146401.
[2] Ferré, G., Haut, T., & Barros, K. (2017). Learning molecular energies usinglocalized graph kernels. The Journal of Chemical Physics, 146(11), 114107.
P2 Reduced Basis Methods for Parameterized PartialDifferential Equations
Supervisors: Pierre-Loïk Rothé and Frédéric [email protected]
ContextMore and more applications require computing many solutions of Partial Differential Equations(PDE’s) for slightly different problems (small changes in coefficients, geometry or boundaryconditions), in a very efficient manner. This is necessary, for instance, in order to get reliablereal-time solutions of parameterized PDE’s or to solve optimization and inverse problems.
Example: modulus of the velocity for a steady Stokes problem, in a given geometry, for twodifferent initial conditions: how to solve this problem for any initial condition in a
few seconds ?
The Reduced Basis Method deals with this issue by creating a special basis that catches thesimilarities between problems. This basis is made of accurate solutions of the problems (calledsnapshots), for some well-chosen parameters, that have been computed in an offline stage. Inthe online stage, the Reduced Basis Method solves the problem for any new parameter usingthe usual Galerkin formulation on the new basis of reduced dimension. This is in contrast with aclassical finite elements basis that would require an extensive number of degrees of freedom toreach a comparable accuracy.
The main computational cost of the method is during the offline stage with the simulation ofsnapshots and the creation of the basis, as the computational cost for the online stageconsisting in solving a linear problem with far less degrees of freedom.The efficiency of the method depends on a trade off between offline and online costs. For real-time applications this method is particularly interesting as the main goal is to reduce the onlinestage computational cost.
ObjectiveThe aim of the project is to understand the Reduced Basis Method and to apply it to thefollowing steady advection-diffusion problem in 1D or 2D:
The goal is to approximate quickly and accurately for different values of the velocity c. Thisvelocity is parameterized by its modulus (and also the angle of the velocity for 2Dapplication). The basis can be built using either a greedy algorithm or a Proper OrthogonalDecomposition (POD). A first step would be a one-dimensional application. And a two-dimensional extension can be considered using the Finite Element software Freefem++.
Book Quarteroni, Alfio, Andrea Manzoni, Andrea Manzoni, and Federico Negri. Reduced basis methods forpartial differential equations: an introduction. Vol. 92. Springer, 2015.
Online material Hesthaven, Jan S., Gianluigi Rozza, and Benjamin Stamm. "Certified reduced basis methods forparametrized partial differential equations." SpringerBriefs in Mathematics. Springer (2016). https://hal-univ-diderot.archives-ouvertes.fr/hal-01223456/document
P3 Machine learning approach for the recognition ofhandwritten arabic characters
Responsible: Said RAGHAY and Oumayma [email protected] and [email protected]
DescriptionThe automatic recognition of characters aims to associate a symbolic
representation to a sequence of graphical symbols. The context of handwrittencharacters recognition arises a big difficulty due to the presence of noise ambiguityand the large variation of writing styles or even the similarity between the entities torecognize especially for the Arabic characters. The characteristics of Arabic scriptsdiffer from Latin ones. Indeed, they have a cursive nature that makes their recognitionprocess more interesting. This process is based on the feature extraction step. Itextracts the features of an element (shape, word, character, etc.) in order to classifyit.
Different styles for the same word
Project objective
The objective of this project is to implement the feature extraction process using Neural Network in Matlab. The Neural Network will be used to perform a deep learning in multiple layers. Paper
Mohamed Elleuch ; Najiba Tagougui ; Monji Kherallah, Arabic handwritten characters recognition using Deep Belief Neural Networks, 12th InternationalMulti-Conference on Systems, Signals and Devices, 2015, http://ieeexplore.ieee.org/document/7348121/
Saad Bin Ahmed, Saeeda Naz, Muhammad Imran Razzak, Rubiyah Yousaf, Deep Learning based Isolated Arabic Scene Character Recognition, Cornall University Library https://arxiv.org/abs/1704.06821
Online course
http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/
Matlab
https://www.mathworks.com/help/nnet/ug/multilayer-neural-networks-and-backpropagation-training.html
P4 Emergence of collective movements :
Application to the birds’ flight. Supervisors: Abdelghani Elmoussaoui, Mohammed El Rhabi and Abdelilah Hakim
[email protected], [email protected] [email protected],
Description:Bird flights have a high level of consistency; however, there does not appear to be an identifiable herd leader: all individuals are similar and follow local rules, without having overall information about the herd. These aggregates possess structuring properties qualitatively comparable to those of the phases of the condensed matter.The aim of this project is to observe and model the behavior of a group of birds. How? We are based on the first model of bird flight: Craig W. Reynolds
Craig W. Reynolds. Flocks, Herds, and Schools: A Distributed Behavioral Model (1987) Craig W. Reynolds. Steering Behaviors For Autonomous Characters (1999)
http://www.red3d.com/cwr/steer/gdc99/.
Initial time Intermediate time Final time
Project Goal
Become familiar with cellular automata and behavioral complexity.
- Observe, model, simulate a flight of birds in 3D via the Craig W. Reynolds model.
Improvement of the Craig W. Reynolds model by taking into account internal / externalfactors (age, shape, wind, climate …)
Bibliography:
“Jonathan RAULT. Complex systems and Agent-based modelling. 2014. 41 p. http://www.environnement.ens.fr/IMG/file/Rault/Slides.pdf
https://drive.google.com/open?id=0ByGb0_TAbBgeMzU5UWdULXRpYk0
P5 “Digitization of documents with a mobile device”
Supervisors: Idriss El Mourabit and Pascal [email protected] and [email protected]
DescriptionThe software will allow the scan of an image of a document (manuscript or printed)captured by a smartphone or a digital camera. Applications are numerous: for example, thefinal user can copy blackboards (or rather here white boards), lecture notes, invoices…Therefore the device will also be a mobile scanner! The user will be able to save thedocument or share it by email or social network. Of course, thie kind of software alreadyexists on mobile devices. Yours should distinguish from the existing ones! Beyond the scanquality, a challenge will be the software's ability to process its input in real time almost.Another advantage of this application is that it helps in automatic recognition of text by anOptical Character Recognition software (OCR).How? By the numerical solution to a partial differential equation, you will get a digitalversion of an image captured by a mobile device. We will assume that only the textualinformation is interesting in the image (even though that is not always the case!).
Image of a receipt photographed byan Iphone 4S
Processed image with the proposedmodel
Goal of the projectPropose and implement a text enhancing algorithm in an image captured under difficult
conditions (noise, variable illumination, shadows…)
Bibliography
Mohammed El Rhabi, Abdelilah Hakim, Zouhir Mahani, Khalid Messou, Sahar Saoud, A Tool for Scanning Document-Images with a Photophone or a Digicam. Computer Applications for Communication, Networking, and Digital Contents. Springer Berlin Heidelberg. 331—341. 2012.
Support (project)
Scan de document-images dans des conditions difficiles d’acquisition, http://elrhabi.free.fr/6perform/Proj_dep_3_6perform_13-14.pdf, Mohammed El Rhabi, 2014.
Application-Qipit
http://www.hametner.com/qipit/
P6 Blur reduction in a photograph of a document
Supervisors: Amine Laghrib and Pascal Monasselaghrib.amine @gmail.com and [email protected]
DescriptionThe goal of this study is to help in the interpretation of information in an image capturedby a smartphone or hand-held camera. The process is technically challenging. The maindifficulty is in the quality of the optical pipeline. It translates into an image that is sub-resolved and possibly blurry. In our study, we will assume the blur is due to the cameraitself (focal plane aberration). For example in barcodes, the unidimensionnal bars merge.Another problem, due to the imaging conditions, is the noise level, depending onhardware, luminosity, etc. It is also unknown.The biggest challenge of such an inverse problem resides in the estimation of the blurlevel and of an approximation of the noise-free image.The blur level estimation is necessary for the interpretation of the data in the image. It hasnumerous applications, which are obvious in the case of text or barcode documents. In afirst stage, students will deal with a simplified model when the blur is known, beforeattacking the general problem, that is, with unknown blur level.From the mathematical standpoint, the model is written u0 = k*u+n, where u0 is theobserved image, k is the noise kernel, supposed defined by a single parameter r, theradius of the uniform kernel. The star represents a convolution and n is the noise,supposed to be independent from the data. Recovering the ideal image u from such anequation is called an inverse problem, such problems are usually ill-posed by themselves,because there is no unique solution. Therefore, a regularization term is introduced in anenergy optimization problem, encoding the desired regularity properties of the solution.
Example of restauration of a partof a business card captured with a smartphone without autofocus (Nokia N70)
Goal of the projectAfter a short theoretical exploration of the problem, the goal of the project will be topropose a numerical algorithm to determine a solution to the blur removal problem for adocument image (containing only text or bar codes).
Specialized booksGilles Aubert, Pierre Kornprobst. Mathematical problems in image processing: Partial
differential equations and the calculus of variations. New York: Springer, 2006.
Patrizio Campisi, Karen Egiazarian (eds.). Blind image deconvolution: theory and applications.CRC Press 2007.
Scientific article
D. Kundur, D. Hatzinakos. Blind image deconvolution, IEEE Signal Processing Magazine, 13(3),1996. pp. 43-64.
P7 Blind Source Separation and Applications
Supervisors: Abdelghani Ghazdali, Abdelilah Hakim and Guillaume Obozinski
Description :In this project, the problem of blind source separation separation (BSS) is considered. The BSSproblem has several applications in different areas including feature extraction, brain imaging,telecommunications, vibratory analysis, industrial reliability ... etc. The problem consists in retrieving unobserved signals (called sources) from unknown mixturesof them (called observations). When the components of the source signals are independent, asolution to this problem exists up to a permutation and scale indeterminacies, and manyalgorithms have been proposed to obtain the solution with applications in many area
BSS problem can be modeled as follows. Denoting A[.] the (unknown) mixing operator, therelationship between the observed and source signals can be written as
x(t) := A[s(t)] + n(t)
where s(t) is the unknown vector of source signals to be estimated, and x(t) represents theobserved signal vector at time t. The goal of BSS, is therefore to estimate the unknown sourcess(t) from the observed mixtures x(t). The presence of additive noise n(t) with in the mixingmodel complicates significantly the BSS problem. It is reduced by applying some form ofpreprocessing such as denoising the observed signals through regularization approach.
The estimation is performed with no prior information about either the sources or the mixingoperator A[.]. Specic restrictions are made on both the mixing model and the source signals inorder to limitthe generality. We will restrict our self to the case where the number of sourcecomponenst and the number of observed mixture ones are equal, and we assume, in thepresent paper, that the mixtures are linear and instantaneous, so that the mixing operator Acan be considered as a pxp matrix. In this case, supposing in addition that A is invertible, thecandidate estimates of the sources will be obviously of the form
y(t) := Bx(t)
where B represents an appropriate demixing matrix. In other words, the problem is to obtainan estimate, denote it B’, \closing" as much as possible to the ideal solution B = A-1, by the useof only the observed vector signal x(t), leading to accurate estimate s’(t) of the source vectorsignal s(t),
s’(t):= B’ x(t)
Project Goal
After a brief theoretical overview of the method, our objective is to propose a numerical one to resolve the problem of blind source separation in the case where the mixture is supposed to be instantaneous and the noise negligible. We will then, apply it to a BSS application of your choice.
Book
Pierre COMON and Christian JUTTEN (éd.). Handbook of Blind Source Separation, Independent Component Analysis and Applications Elsevier 2010, Academic Press, 2010, ISBN: 978-0-12-374726-6.
Paper
A. Ghazdali, M. El Rhabi, H. Fenniri, A. Hakim, A. Keziou, Blind noisy mixture separation for independent/dependent sources through a regularized criterion on copulas, Signal Processing, Volume 131, February 2017, Pages 502-513, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2016.09.006.
P8 Deblurring algorithm for licence plate images
Supervisors: Amine Laghrib and Abdelilah [email protected]
Description:
An image that has been subject to the out-of-focus phenomenon has reduced sharpness, contrast and level of detail depending on the amount of defocus. To restore out-of-focused images is a complex task due to the information loss that occurs. However there exist many restoration algorithms that attempt to revert this defocus by estimating a noise model and utilizing the point spread function. To identify the licence plate of a vehicle, the captured image must be sharper and readable. However, since the captured image is always degradedby different factors like the atmosphere, restoration algorithms are needed.
The main purpose:The purpose of this project is to propose a robust algorithm that can restoreefficiently the degraded licence plate images. In fact, the main object is to use aprimal-dual algorithm to resolve the optimization problem with a total variation(TV) regularization. In figure , we present an example of the main purpose ofthis project
Bibliography:
Svoboda, P., Hradi, M., Mark, L., and Zemck, P. (2016, September).CNN for license plate motion deblurring. In Image Processing (ICIP),2016 IEEE International Conference on (pp. 3832-3836). IEEE.
Chambolle, A., and Pock, T. (2011). A first-order primal-dual algorithmfor convex problems with applications to imaging. Journal of Mathemat-ical Imaging and Vision, 40(1), 120-145.
P9 Analytical approach for the problem of rigidbodies with simultaneous contacts
Supervisors: Pierre Argoul, Abdelilah Hakim and Aïssam Jebane [email protected], [email protected]
and [email protected] Abstract: In the context of non-regular mechanics, the principal laws of behavior used to model shocks between solids are derived from an associated quadratic law or from the Coulomb's law.The state of the system after the shock is described in an implicit way, thus the resolution of the equation of the shock is equivalent to approaching a minimization problem by rather com-plex iterative methods, according to the used law, like the method of duality and gradient methods. The analytical formulae quantifying the speed of rigid disks (2D) after a shock are determinedin a predictive way, as well as the percussion induced by the shock. To handle the case of sim-ultaneous contacts, a method of superposition is used. Numerical simulations are then presen-ted for simultaneous shocks of rigid disks to demonstrate the efficiency of the proposed solu-tion. The results are compared to those obtained with classical numerical algorithms used to solve this type of problem.
(a) (b) (c)Figure 1: (a) Initial configuration, (b) Comparison of the computation time as a function of the number of contacts, the time t_1 (in red ) corresponds to the analytical solution, the time t_2 (in blue) corresponds to the solution obtained with “quadprog” function and t_3 (in black) cor-responds to Uzawa’s method. (c) Error comparison: Err_2 (in pink) error in Euclidean norm between the analytical solution and that obtained by the Uzawa’s method, Err_1 (in blue) the error the analytical solution and that obtained Using the "quadprog" function. See the link: Simulation: Simultaneous shock of disks
Objective of the projectGeneralize the proposed method to the case of balls (3D) figure (2-a) and to the
case of any form solids (figure 2-b).
(a) (b) Figure 2
References: (link) Simultaneous collision of disks
P10 « Communication audiovisuelle »
Supervisor: Karima [email protected]
Descriptif
L’image constitue un outil moderne de narration et de valorisation d’un événement ainsique de diffusion des connaissances. L’objectif de ce projet est de réaliser une vidéo d’un format court (5 à 7 minutes). Cette vidéo retracera tous les moments forts du voyage de département au travers un montage dynamique et participera ainsi à mieux faire connaitre la qualité et l’originalité de la form-ation IMI. Elle nécessitera donc, en amont, une réflexion sur le type d’images qui seront tournées, l’insertion éventuelle d’extraits d’interviews, la recherche de sons libres de droits, des textes écrans etc. Elle sera destinée à être mise en ligne sur le site web de l’École et relayée sur ses autresoutils de communication : facebook, twitter, lettre d’information mensuelle adressée à unfichier très large de contacts et de partenaires. Les élèves pourront s’appuyer sur les conseils de Karima Chelbi, adjointe à la directrice dela communication de l’École.
Un exemple de vidéo décrivant le voyagede département IMI 2014-2015
Un exemple de vidéo décrivant un projetde département IMI 2014-2015
P11 « L'innovation comme création destructrice de valeur »
Supervisors: Alon Rozen and Abdelkader [email protected] and [email protected]
Descriptif
L’explication du cycle des affaires par le rythme d’émergence des innovationstechnologiques peut être utilisée comme grille de lecture dans le management et la misesur le marché des produits innovants. Nous retiendrons comme point de départ latypologie des innovations chez Schumpeter (innovation de produits, de procédés, demodes de production, de débouchés, de matières premières). Nous présentons commegrille d’analyse prospective du cycle des affaires les étapes suivantes :
a) La stratégie de l'entrepreneur innovant pour conquérir un marché et évincer laconcurrence dont les produits deviennent obsolètes (destruction de valeur)
b) L'évolution des prix et des profits sur le marché si l'innovation permet d'atteindreune position de monopole et d'obtenir une rente (création de valeur)
c) La réaction du marché: les imitateurs qui copient l'innovateur, ce qui intensifie laconcurrence et réduit le profit jusqu'à annuler la rente
d) A long terme, et à l'équilibre général walrassien des marchés en condition de concurrence le profit devient nul!
Conclusion l'innovation est seule source de profit à travers le processus de destruction créatrice.
Dans le cadre de ce projet, nous proposons d’étudier le processus itératif decommercialisation des innovations :
1) Identification des usages des consommateurs par des méthodes relevantnotamment du design thinking.2) Développement de fonctionnalités répondant aux usages3) Elaboration de business model : étude de marché comprenant l’estimation de ladisposition à payer des consommateurs et l’analyse de la concurrence.
But du projetL’objectif de l'atelier est que les élèves appliquent ce processus à travers un exemple concret d'innovations passées (internet rend obsolète le minitel, le smartphone et le bibop...) ou à venir, en se basant sur des données pertinentes et fiables.
Références
SCHUMPETER, Joseph (1911) : Théorie de l'évolution économique. Recherches sur le profit,le crédit,l'intérêt et le cycle de la conjoncture. Paris : Dalloz, 1999.
AGHION, P., BLOOM, N., BLUNDELL, R., GRIFFITH, R. and HOWITT, P. (2005), «Competition and Innovation: An inverted U Relationship», Quarterly Journal of Economics, May 2005, pp. 701-728.
P12 Méthodes sparse pour l’estimation de variance-covariance et l’ACP
Supervisors: Ernesto Palidda, Aurélien Alfonsi and Bernard [email protected],
[email protected] and [email protected]
Description
Determining the dependence structure between assets and/or risk factors is at thecore of multi-dimensional financial modelling problems. For example when selecting anoptimal portfolio or when calibrating a multidimensional interest rates model. Thedependence structure is often represented as a variance-covariance or correlationmatrix. PCA is also a classical method to determine the structure of a given dataset.When the data is noisy, classical methods for covariance selection or PCA give poorresults, they are biased by the noise and do not allow to determine a relevantdependence structure.
Sparse methods are used in different fields to solve similar problems. The idea is tolook for a "sparse" solution, meaning that we look for a solution in a constrained spacewhere we impose that most of the entries of the solutions are zero. Sparse-typeconstrains are expressed with respect to the cardinal, or on a finite number ofelements of a vector or matrix. The resulting optimisation problem is a combinatorialproblem and often it is a hard problem. Convex relaxation techniques solve an"equivalent" convex problem which approximate the original problem.
Objective
The aim of the project is to implement a tool for sparse covariance selection or/andPCA. We could use algorithms based on convex relaxation techniques and apply thesemidefinite programming toolbox to solve the problem. We will apply the toolsdeveloped to financial data.
Références
A. d'Aspremont, L. El Ghaoui, M. I. Jordan, G. R. G. Lanckriet, A Direct Formulation forSparse PCA Using Semidefinite Programming.
B. A. d'Aspremont, O. Banerjee, L. El Ghaoui, First-Order Methods for Sparse Covariance Selection.C. R. Luss, A. d'Aspremont, Clustering and Feature Selection using Sparse Principal Component Analysis.D. O. Banerjee, L. El Ghaoui, A. d'Aspremont, Model Selection Through Sparse Maximum Likelihood Estimation.E. A. d'Aspremont, F. Bach, L. El Ghaoui, Optimal Solutions for Sparse Principal Component Analysis.
P13 Blockchain & impact économique : étude d’un caspratique
Supervisors : Nadia Filali and Abdelkader [email protected] and [email protected]
DescriptifLe but de cette étude est de s’initier à la blockchain et ses (nombreuses) applications en économie.Cette technique, issue du peer to peer, va évidemment révolutionner les systèmes de paiement etla monétique, et invite à repenser la notion même de monnaie. D’un point de vue pratique, leprincipe général d’une blockchain est une chambre de compensation modélisée par un ensemble debloc, protégé contre toute modification, dont chacun contient l’identification de son prédécesseur.Elle peut avoir la forme d’un arbre doté de plusieurs branches]. Un exemple de bockchain particulierest le bitcoin, une devise numérique pour les transactions en ligne sans tiers de confiance]. Il estutile de souligner qu’il existe de nombreuses autres applications (que la monnaie). Ainsi, denombreuses startups se sont lancées dans la création de chaînes de valeurs autour de lacertification de documents (diplômes, carte grise, testament …), dans l’IoT et la smart grid. Dans ceprojet, nous aimerions considérer le cas du change et du transfert de fonds.
But du projet
Après un bref survol théorique de la méthode, le but de ce projet sera de proposer une étuded’impact de la blockchain dans le cas particulier du change et du transfert de fonds. Il faudradémontrer la fiabilité technique du système de certification et de paiement mais prendre en comptela contrainte de viabilité économique.
Articles
Survey on Blockchain Technologies and Related Services:
http://www.meti.go.jp/english/press/2016/pdf/0531_01f.pdf
https://cryptofr.com/topic/2620/la-caisse-des-d%C3%A9p%C3%B4ts-lance-officiellement-l-
initiative-de-place-blockchain
https://regulation.revues.org/11489 Ref2
http://www.lemonde.fr/societe/article/2016/05/25/le-compte-sans-banque-affole-les-
compteurs_4925818_3224.html
http://www.lemonde.fr/economie/article/2015/09/30/la-revolution-blockchain-legs-du-bitcoin-
en-version-seduction_4778603_3234.html
http://www.lefigaro.fr/secteur/high-tech/2016/03/24/32001-20160324ARTFIG00317-macron-
amenage-la-loi-pour-tester-la-blockchain-sur-la-finance.php
http://cermics.enpc.fr/~delara/ARTICLES/Les_defis_de_la_gestion_optimisee_des_smart-
grids.pdf
P14 Blockchain, IoT, securtity, smart city (àconfirmer)
Supervisors: Louis Grandboulan and Eric [email protected]
Descriptif A confirmer demander à M. El Rhabi ….
P15 Transport du futur : innovation en rupture ?
Supervisors: Eric Duceau and Saïd [email protected]
Descriptif
Ce projet se présente comme une question stratégique pour un certain nombre d’acteurs dutransport, du tourisme et des infrastructures : « est-il possible de voir arriver un nouveau moded’organisation des déplacements sous l’impulsion d’une plateforme de service ? Si oui, dansquelle partie du transport ? En conséquence, qui pourraient être les perdants, les gagnants ? »Dans le jargon des consultants en stratégie managériale, on dirait : « est-ce que le transportpeut être Uberisé ? »
Ainsi posée, cette question est trop vaste, trop vague, trop superficielle pour avoir un intérêt ;c’est cependant sous cette forme que vous (ingénieurs des Ponts, diplômés dans quelquesmois !) la recevrez. Une démarche d’ingénieur est donc nécessaire pour construire des éléments de réponse ; Vu letemps disponible, nous travaillerons comme une équipe de réponse à appel d’offre :brainstorming, analyse, classification des problèmes, choix selon les critères de l’analysecoûts/bénéfices et vous construirez une proposition technique permettant de répondre à laquestion (application pour smartphone) ; durant cette semaine, vous ne pouvez qu’envisagercomment vous y prendre ! soyez innovants dans la construction de la réponse à appel d’offrepour pouvoir être innovants (si vous gagnez) dans la réponse à la question.
Le déroulement que nous vous proposons est le suivant (mais votre équipe est seulesouveraine et vous pouvez choisir un mode de travail différent !) :Vous prenez connaissance du thème maintenant et vous devez récolter un premier jetd’information avant la semaine du 19 septembre (internet suffit, on ne sait pas encore ce quiva servir). Votre expérience personnelle, quelques questions autour de vous ; allez voir lescaractéristiques du transport aérien par exemple ; bref, ne découvrez pas complétement laproblématique.Le 19 matin, E Duceau animera la séance de brainstorming dans laquelle vous échangerezvotre compréhension de la question (animera ne veut pas dire qu’il fait un cours ! c’est vousqui amenez une partie de la matière).Le 19 après-midi : mise en forme du brainstorming ; liste des parties prenantes, desinteractions ; identifications des points durs, des manques ; constitution de sous-groupes pouraffiner des questions.Probablement le 20 : une rencontre possible (souhaitable ?) avec A. Slifi sur les marchés biface,les caractéristiques des plateformes de désintermédiation, des modèles économiques sous-jacents ; ie l’équipe de réponse à appel d’offre rencontre un expert des modèles économiques.Probablement le 20 : une rencontre possible (souhaitable ?) avec V. Leclère et E. Duceauconcernant les modélisations possibles des interactions entre acteurs ; ie l’équipe de réponse àappel d’offre rencontre des experts des modélisations complexes. Le mercredi, l’axe de la proposition à rédiger doit être décidé ; répartition des rôles pour larédaction (qui prend la forme d’une présentation ppt dont vous discuterez la forme, lalongueur, le niveau de détail).Vendredi : rassemblement des contributions, répétition, tests ; puis présentation devant lejury ; l’équipe de réponse à appel d’offre rencontre un panel de ses clients potentiels !
But du projet
Le but de ce projet est d’écrire une proposition techniquement fiable et économiquementviable en direction d’un consortium d’industriels qui voudraient lancer une étude d’opportunitésur le sujet. La présentation doit donc présenter une problématique et un plan de résolutionmais ne peut pas prétendre répondre à la question. Néanmoins, les éléments technologiques,méthodologiques, financiers avancés donneront de la valeur à la proposition.
Ci-après, des acteurs actuels du (marché du) transport ; la chaine de valeur peut-elle être bousculée par une plateforme « organisant » le déplacement « point à point » par exemple ?
Quelques pistes
Vos collègues de l’an passé ont traité le cas du temps perdu à l’arrivée sur l’aéroport (leur poster doit être quelque part ! et vous donnera des idées sur les acteurs)
Ouvrages spécialisés conférence de Jean Tirole
http://www.microeconomix.fr/sites/default/files/import2/111007%20Economic%20Focus%20-%20Concentrations%20sur%20les%20marches%20bifaces.pdf
https://halshs.archives-ouvertes.fr/halshs-00455382/document
http://www.tresor.economie.gouv.fr/File/395781
Pensez transport en Europe/US mais aussi en Asie : marchés du futur ?
P16 Matching of postal addressesSupervisor: Xavier Clerc
Description
Open data gives access to large sets of data produced by different entities. Most often, it isnecessary to merge data sets in order to produce new data. However, this is not always atrivial task, especially when one has to deal with free-form text or loosely-structured text.Postal addresses fall into this category, whether the address is split into components (e.g.number, street, city, zip code) or not, for a number of reasons:- when the text has been user-entered, typos can occur;- abbreviations may be used (e.g. "st" for "street");- given names may be abbreviated of elided (e.g. "Frédéric et Irène Joliot-Curie" vs "F. et
I. Joliot-Curie" vs "Joliot-Curie");- capitalization may be different, possibly leading to the modification of some letters
(e.g. "rue des Réservoirs" vs "RUE DES RESERVOIRS");- the relationship between city and zip code may not be bijective (i.e. addresses differing
only by zip code may actually be equivalent);- etc.
Or course, a number of web services are available to normalize addresses (and/or convertthem into latitude/longitude coordinates). However, it is not possible to use them because:
4. some services, such as Google Maps, may return slightly different results for thesame request (e.g. "Paris" vs "Paris 5ème arrondissement");
5. they induce a network request whose latency may be too high;6. some data sets to be merged may be so large that it would imply to issue millions
of requests;7. etc.
As a result, it is useful (if not mandatory) to be able to perform the matching of postaladdresses through an in-house process. The goal of the project is to develop a prototypeallowing to:
- check whether two addresses should be considered as equivalent;- efficiently look up for an address into a set of addresses.
P17 Storage calibration for a solar power plant
Supervisor: Henri [email protected]
DescriptionToday, the storage of electricity is considered to be a major challenge for the integration ofrenewable energies on the electricity grid. Indeed, these energies are both intermittent andhighly variable: production varies greatly over time and is diffi-cult to predict. To smooththe production and ensure that supply equals demand, energy can be stored during o-peakperiods and then restored when the demand is high.Storage technologies: We consider here the dimensioning of the storage associated witha solar power plant. We can study several forms of storage for energy:
Chemical: Electric batteries (lithium-ion, sodium-sulfur), hydrogen, etc. Mechanical: Pumping station, inertia wheel, etc.
These didifferent technologies have different response times and capacities, which impact theperformance of storage management. It will therefore be necessary to make a study of thetechnology the most adapted according to the situation and the conditions of the plant.The initial investment cost depends also on the choice of storage technology.Storage management: Several uncertainties have to be taken into account for the management of the storage: the selling price of electricity on the SPOT market and solarradiation.This randomness can be modeled by random variables. A statistical study may be realized to model them as best as possible, for example by using techniques derived from the theory of time series or machine learning. The more precise the modeling, the more effi-cient the storagewill perform. In this way, intelligent control methods can be proposed using stochastic optimization algorithms. The aim is to obtain an estimate of the operational costs of managing the solar power plant.Risk management: Several methods exist to measure the risk of optimal decisions.A study could be carried out to illustrate the impact of risk aversion on the design and management of the solar power plant. If chosen, this study will replace the statistical studyof randomness.Economic study: Investment and operating costs will be taken into account for the realization ofan economic study that will allow to quantify the portability of the project.Goal of the projectTo propose and implement an algorithm to optimally size the storage associated with asolar power plant. The idea is to eventually obtain a prototype of decision support software.
ReferencesPhilippe Artzner, Freddy Delbaen, Jean-Marc Eber, and David Heath. Coherent measures
of risk. Mathematical nance, 9(3):203ﾧ 228, 1999.Richard E Bird and Roland L Hulstrom. Simplified clear sky model for direct and
diffuse insolation on horizontal surfaces. Technical report, Solar Energy Research Inst.,Golden, CO (USA), 1981.
Michel De Lara, Pierre Carpentier, Jean-Philippe Chancelier, and Vincent Leclere.Optimization methods for the smart grid. Report commissioned by the Conseil Françaisde l'Énergie, École des Ponts ParisTech, 2014.
Pierre Haessig. Dimensionnement et gestion d'un stockage d'énergie pour l'atténuationdes incertitudes de production éolienne. PhD thesis, École normale supérieure deCachan-ENS Cachan, 2014.
Detlev Heinemann, Elke Lorenz, and Marco Girodo. Forecasting of solar radiation.
« Oser ; le progrès est à ce prix.Toutes les conquêtes sublimes sont plus
ou moins des prix de hardiesse. »Victor Hugo, Les Contemplations, 1856
