Workshop on “Image and Physics” on June 18, 2021, Center of Mathematical Morphology and Center of Materials

Workshop on “Image and Physics” on June 18, 2021, Center of Mathematical Morphology and Center of Materials

We are pleased to announce a workshop centered on “image and physics” on June 18, 2021, organized by the Center of Mathematical Morphology and Center of Materials, devoted to image analysis, deep learning, physics, and material science. The main objective of this event is to promote exchanges in an informal setting between researchers of MINES ParisTech working on image analysis and deep learning, on the one hand, and on physics and material science in general. You are welcome to send your contributions (title, authors, and abstract) to the organizers before June 4th.

We hope to see you in June,

François Willot et Henry Proudhon

18/06/2021 at 9:30.link : https://mines-paristech.zoom.us/j/92437426470?pwd=YWlIblpndkRRTlQyQXpZK1pJaXY4QT09
<https://mines-paristech.zoom.us/j/92437426470?pwd=YWlIblpndkRRTlQyQXpZK1pJaXY4QT09>

(ID de la réunion / Meeting ID : 92437426470 Mot de passe / Password :
280307)

Piergiorgio Vitello: Enhancing Public Transport Demand Information Using Crowdsourced Data

Piergiorgio Vitello: Enhancing Public Transport Demand Information Using Crowdsourced Data

Machine Learning Seminar presentation

Topic: Enhancing Public Transport Demand Information Using Crowdsourced Data

Speaker: Piergiorgio Vitello, Faculty of Science, Technology and Medicine; University of Luxembourg

Time: Wednesday, 2021.06.16, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

In the era of Advanced Public Transport Systems, new technologies have been introduced and deployed providing multiple sources of data that can be utilized for demand estimation and analysis. In this study, we investigate the potential of a crowdsourced dataset such as Google Popular Times to quantify the correlation of the demand of a transit line in conjunction with the level of attractivity of its station and the surrounding local businesses to understand to which extent the characteristics of the activities in the area can be associated with the passenger transference at the stops.

Additional material:

MMLDT-CSET 2021 NSF Fellowship Application and registration

MMLDT-CSET 2021 NSF Fellowship Application and registration

Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET 2021)

An IACM Conference September 26-29, 2021 – Hyatt Regency Mission Bay, San Diego, USA

(website: https://mmldt.eng.ucsd.edu)  

Co-sponsors: IACM, ASCE-EMI, IUTAM, USNC/TAM, USACM

 

1. NSF Fellowship Application (https://mmldt.eng.ucsd.edu/fellowship)

The NSF Fellowships are available to promote awareness of mechanistic machine learning and digital twins and knowledge dissemination to all levels in education, including high school, undergraduate, and graduate levels. A portion of the fellowship will be reserved for underrepresented groups to promote equity, diversity, and inclusiveness.

This NSF Fellowship will cover the conference and short course remote registration fees. Only US Citizens and Permanent Residents are eligible for NSF Fellowships.

1). NSF Fellowship for 𝐠𝐫𝐚𝐝𝐮𝐚𝐭𝐞 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐩𝐨𝐬𝐭𝐝𝐨𝐜 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫𝐬 to attend the MMLDT-CSET Conference and Short Course 2.

2). NSF Fellowship for 𝐡𝐢𝐠𝐡-𝐬𝐜𝐡𝐨𝐨𝐥 𝐭𝐞𝐚𝐜𝐡𝐞𝐫𝐬 𝐚𝐧𝐝 𝐚𝐝𝐦𝐢𝐧𝐢𝐬𝐭𝐫𝐚𝐭𝐨𝐫𝐬 to attend the MMLDT-CSET Conference and Short Course 1.

3). NSF Fellowship for 𝐮𝐧𝐝𝐞𝐫𝐠𝐫𝐚𝐝𝐮𝐚𝐭𝐞 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 to attend the MMLDT-CSET Conference and Short Course 1.

 

2. Early Registration (https://mmldt.eng.ucsd.edu/register)

The Conference and Short Courses Early Registration is open until June 30, 2021. The conference will be organized with a hybrid format consisting virtual and on-site sessions in 44 Mini-symposia under 8 Tracks.

On-Site Attendance Includes:

Registration fee covers Ice Breaker Reception on Sunday, Pre-Banquet Reception and Banquet on Tuesday, Coffee Break Refreshments, Panel Discussions, on-site plenary lectures and technical presentations, and remote technical presentations.

Virtual Attendance Includes:

Registration fee covers live-streamed on-site plenary lectures and remote technical presentations.

 

Panel Discussions: 

Mechanistic Data Science for High School and Undergraduates STEM Education and Applications

 

Short Courses:

1). Mechanistic Data Science (MDS) for STEM Education and Applications

2). Mechanistic Machine Learning for Engineering and Applied Science

 

 Organizing Universities 

UC San Diego, Northwestern University, Stanford University, Brown University, Columbia University, and Arts et Métiers Institute of Technology of France

 

Feel free to contact the conference organizers with any questions or concerns: ceer.ucsd@gmail.com 

Juntong Chen: Robust estimation of a regression function in exponential families

Juntong Chen: Robust estimation of a regression function in exponential families

Machine Learning Seminar presentation

Topic: Robust estimation of a regression function in exponential families

Speaker: Juntong Chen, Faculty of Science, Technology and Medicine; University of Luxembourg

Time: Wednesday, 2021.06.02, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

In this talk, we consider the problem of estimating a regression function when the distribution of the data belongs to an exponential family. Several interesting problems are under this setting, for example, logit, Poisson, and exponential regressions. We first introduce these three estimation problems and then solve them by means of a new estimation procedure which is called Rho-estimation. Finally, we carry out a simulation study for illustrating and discussing the theoretical properties of rho-estimators as compared to the well-known maximum likelihood ones.

Additional material:

Jimmy Tekli: Leveraging Deep Learning-Assisted Attacks against Image Obfuscation via Federated Learning

Jimmy Tekli: Leveraging Deep Learning-Assisted Attacks against Image Obfuscation via Federated Learning

Machine Learning Seminar presentation

Topic: Leveraging Deep Learning-Assisted Attacks against Image Obfuscation via Federated Learning

Speaker: Jimmy Tekli, BMW GROUP, Université de Franche-Comté

Time: Wednesday, 2021.05.19, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

Federated learning (FL) has recently gained much attention as a machine learning setting where multiple clients collaborate in solving a machine learning problem under the coordination of a central server/coordinator. Each client’s raw data is stored locally without being exchanged nor transferred to the central server; instead, the model’s parameters are shared/aggregated and used to achieve the learning objective. Throughout this seminar, we first present the FL concept, the Federated Averaging algorithm, the FL client/server architecture along its challenges/limitations and applications. Second, we demonstrate how we employed FL as a collaborative adversarial strategy to leverage deep learning-assisted attacks against obfuscated (e.g. blurred) face images.

Additional material:

Fateme Darlik: Neural network supported surrogate models for particle-laden flow

Fateme Darlik: Neural network supported surrogate models for particle-laden flow

Machine Learning Seminar presentation

Topic: Neural network supported surrogate models for particle-laden flow

Speaker: Fateme Darlik, Faculty of Science, Technology and Medicine; University of Luxembourg

Time: Wednesday, 2021.05.12, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

This project focuses on coupling a data-driven model in conjunction with CFD (Computational Fluid Dynamics) to predict the behavior of biomass particles in a fixed bed. This problem (particle-fluid problem) can be solved by two-way coupling CFD and XDEM. Since this methodology is often computationally expensive, two solutions are proposed. Firstly, the neural network (using TensorFlow) is used as a surrogate model to replace XDEM. Afterward, this surrogate model is coupled with the CFD method to solve the particle-fluid problem employing preCICE (Precise Code Interaction Coupling Environment). An alternative approach assumes the behavior of dense particles in the biomass bed similar to that of fluid (of unknown material parameters). The neural network is used to identify the properties of the fluid. Having the properties of the fluid, the CFD method is used solely to solve the mentioned biomass problem.

Additional material:

Anja Leist: A classification of machine learning approaches for the social and health sciences

Anja Leist: A classification of machine learning approaches for the social and health sciences

Machine Learning Seminar presentation

Topic: A classification of machine learning approaches for the social and health sciences

Speaker: Anja Leist, Faculty of Humanities, Education and Social Sciences; Department of Social Sciences; University of Luxembourg

Time: Wednesday, 2021.05.05, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, among other reasons due to the importance of incorporating domain knowledge into the strategy of data analysis in the social and health sciences. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches. We map established distinctions in data science for the purposes of description, prediction, and causal inference to common research goals. Example applications to predict prison violence, assess the prevalence of non-communicable diseases, and explain adverse birth outcomes are presented. The meta-mapping should improve the understanding between computational disciplines and the social and health sciences.

Additional material:

Pavlos Gkinis: Acceleration of CFD calculations using Reduced-Order Modeling. Application in CVD processes

Pavlos Gkinis: Acceleration of CFD calculations using Reduced-Order Modeling. Application in CVD processes

Machine Learning Seminar presentation

Topic: Acceleration of CFD calculations using Reduced-Order Modeling. Application in CVD processes

Speaker: Pavlos Gkinis, National Technical University of Athens

Time: Wednesday, 2021.04.28, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

A computational data-driven framework for the development of Reduced Order Models (ROMs) with application to Chemical Vapor Deposition (CVD) reactors is presented.[1] Describing and predicting the behavior of CVD reactors requires complex high-fidelity Computational Fluid Dynamics (CFD) models with millions of unknowns, the solution of which has a significantly high computational cost, making it imperative to develop ROMs. The reduction of the dimension of the problem is based on the combination of the Method of Snapshots (MoS) which is a variant of Proper Orthogonal Decomposition (POD) and properly trained Artificial Neural Networks (ANN). Snapshots are the sequential states of the reactor during the transition from one steady-state to another after a certain disturbance on one of the controlled parameters. These are computed by a simplified, low-fidelity, and time-dependent CFD model without chemical reactions. The aim is to develop a ROM with satisfactory accuracy, based on low-fidelity data obtained at low computational cost. The snapshots are used to find the dominant eigenvectors that span the solution space, as well as to train neural networks to quickly predict the time-dependent coefficients of the ROM. The model prediction is certainly not satisfactory due to the low-fidelity data used in the development of the ROM, but it is sufficient that when fed into a complete and large-scale process model, the solution convergence occurs in significantly less time. In conclusion, a remarkable acceleration of calculations is achieved, that reduces the computational cost of parametric analysis and the general study of the prevailing physical[2] and chemical phenomena[3] of the process.

Additional material:

[1] P. A. Gkinis, E. D. Koronaki, A. Skouteris, I. G. Aviziotis, A. G. Boudouvis. Chemical Engineering Science 199 (2019) 371-380.
[2] E. D. Koronaki, P. A. Gkinis, L. Beex, S. P. A. Bordas, C. Theodoropoulos, A. G Boudouvis. Comput. & Chem. Eng. 121 (2019) 148-157.
[3] R. Spencer, P. A. Gkinis, E. D. Koronaki, D. I. Gerogiorgis, S. P. A. Bordas, A. G. Boudouvis, Comput. & Chem. Eng. 149 (2021) 107289.

Raphaël Couturier & Michel Salomon: Some applications of deep learning from image security, to medical image, through IoT physiological signal

Raphaël Couturier & Michel Salomon: Some applications of deep learning from image security, to medical image, through IoT physiological signal

Machine Learning Seminar presentation

Topic: Some applications of deep learning from image security, to medical image, through IoT physiological signal

Speaker: Raphaël Couturier & Michel Salomon, FEMTO-ST Institute, University Bourgogne Franche-Comté (UBFC), CNRS

Time: Wednesday, 2021.04.21, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

In this presentation, we will explain very quickly many applications on which we had the opportunity to work. We will talk about steganalysis, watermark removing, myocardium segmentation after a heart attack, 1d physiological signal compression, and reconstruction, airplane protocol attack detection, autofocus with digital holography. Moreover, we will present 4 collaborations with practical applications: Faurecia, Colruyt and BMW, Firemen.

Additional material: