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)

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 

Research Associate (Postdoc) in Reduced Order Modelling for Urban Wind Flow Application

Research Associate (Postdoc) in Reduced Order Modelling for Urban Wind Flow Application

With the acceleration of population growth and increasing urbanization, the standard archetype of a building being used as a shelter only needs to move forward to an energy self-sufficient building. DATA4WIND project presents a solution in achieving this vision faster by relying on the urban wind power utilization. The goal of the DATA4WIND project is to assure reliable prediction of aerodynamic information on local wind flow patterns essential for urban wind power utilization introducing new approaches. One approach is based on hybrid data assimilated platform and its strategy enabling synergy between computational and experimental wind engineering. An additional track of DATA4WIND project concerns the development of a computationally less demanding approach using reduced modeling techniques that satisfy the main prerequisite – sufficient accuracy of numerical predictions.

For more details, please http://emea3.mrted.ly/2odk6.

Hichem Omrani: Predicting air pollution using remote sensing and sensors measurement

Hichem Omrani: Predicting air pollution using remote sensing and sensors measurement

Machine Learning Seminar presentation

Topic: Predicting air pollution using remote sensing and sensors measurement

Speaker: Hichem Omrani, Luxembourg Institute of Socio-Economic Research (LISER)

Time: Wednesday, 2021.03.31, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

Air pollution is a threat to public health, having negative effects on human health and well-being. This research seminar aims to (1) predict air pollution at a fine spatial resolution using the “land use regression” model, (2) assess population exposure to air population in Luxembourg and its surrounding areas, (3) and discuss ongoing work, opportunities and challenges for future research.

Additional material:

Gabriele Pozzetti: An Industry view on ML: CERATIZIT technology landscape and the art of collaborating with an industrial partner.

Machine Learning Seminar presentation

Topic: An Industry view on ML: CERATIZIT technology landscape and the art of collaborating with an industrial partner.

Speaker: Gabriele Pozzetti, Manager \ Artificial Intelligence and Data Science \ CERATIZIT

Time: Wednesday, 2021.03.24, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

The difference between theory and practice is that in theory the two will mostly agree, in practice they will not.

CERATIZIT is a global player in high-performance material in the midst of its digitalization. ML is nowadays a pervasive technology with plenty of possible applications for an innovative industrial player. But what differentiates a successful project from a failing one? How can a researcher extract value from data instead of using valuable resources to generate data?

This talk aims at giving you an idea of some of the main applications of ML currently active at CERATIZIT and some tips to make your next industrial partnership a success.

Additional material:

 

Lars Beex: Unsupervised learning to select modes for reduced-order models of hyperelastoplasticity: application to RVEs

Machine Learning Seminar presentation

Topic: A Physics-guided Machine Learning Model Based on Peridynamics

Speaker: Dr Lars Beex, FSTM, University of Luxembourg

Authors: S. Vijayaraghavan (1; 2), L.A.A.Beex (1), L.Noels (2), and S.P.A.Bordas (1)

1) University of Luxembourg, Ave. de la Fonte 6, L-4364 Belval, Luxembourg.

2) University of Liege, CM3 B52, Alle de la découverte 9, B4000 Liege, Belgium

Time: Wednesday 2021.01.13, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

Many model order reduction approaches use solutions of a few ‘offline’ training simulations to reduce the number of degrees of freedom of the many ‘online’ simulations of interest. In proper orthogonal decomposition, singular value decomposition is applied to a matrix with the training solutions in order to capture the most essential characteristics in the first few modes – which are used as global interpolation bases.

Proper orthogonal decomposition has proven itself as an accurate reduced-order model approach for elliptical partial differential equations. In the field of solid mechanics, this means that it is accurate for (hyper)elastic material models, but not for (hyper)elastoplasticity. Based on the study of [1], the current contribution investigates how clustering of the training solutions and extracting global modes from each cluster can improve the accuracy of proper orthogonal decomposition for hyperelastoplasticity. Both centroid-based clustering (i.e. k-means clustering) and connectivity-based clustering (based on Chinese whispers) are investigated.

The approach is applied to a hyperelastoplastic representative volume element exposed to monotonic loading, quasi-monotonic loading, and quasi-random loading. In case of monotonic and quasi-monotonic loading, the components of the macroscopic deformation tensor are the variables to which clustering is applied. In case of quasi-random loading, however, not only the components of the macroscopic deformation tensor and the incremental changes of these components are the variables to which clustering is applied, but also all history variables of all integration points.

Additional material:

[1] David Amsallem, Matthew J. Zahr and Charbel Farhat. Nonlinear model order reduction based on local reduced-order bases. Int. J. Numer. Meth. Engng. VOL 92 IS-10 SN-0029-5981

Tittu Mathew: Bayesian uncertainty quantification and model selection

Machine Learning Seminar presentation

Topic: Bayesian uncertainty quantification and model selection

Speaker: Tittu Mathew, Indian Institute of Technology Madras | IIT Madras · Department of Mechanical Engineering

Time: Wednesday 2020.11.11, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

Adaptive Importance Sampling based Neural Network framework for Reliability and Sensitivity prediction for Variable Stiffness Composite Laminates with hybrid uncertainties

In this work, we propose to leverage the advantages of both the Artificial Neural Network (ANN) based Second-Order Reliability Method (SORM) and Importance sampling to yield an Adaptive Importance Sampling based ANN, with specific application towards failure probability and sensitivity estimates of Variable Stiffness Composite Laminate (VSCL) plates, in the presence of multiple independent geometric and material uncertainties. The performance function for the case studies is defined based on the fundamental frequency of the VSCL plate. The accuracy in both the reliability estimates and sensitivity studies using the proposed method were found to be in close agreement with that obtained using the ANN-based brute-force Monte Carlo Simulations (MCS) method, with a significant computational savings of 95%.

Moreover, the importance of taking into account the randomness in ply thickness for failure probability estimates is also highlighted quantitatively under the sensitivity studies section.

Additional material:

[1] Tittu Varghese Mathew, P. Prajith, R.O. Ruiz, E. Atroshchenko, S. Natarajan, Adaptive importance sampling based neural network framework for reliability and sensitivity prediction for variable stiffness composite laminates with hybrid uncertainties, Composite Structures, 2020 https://doi.org/10.1016/j.compstruct.2020.112344

 

 

 

Cosmin Anitescu: Methods Based on Artificial Neural Networks for the Solution of Partial Differential Equations

Machine Learning Seminar presentation

Topic: Methods Based on Artificial Neural Networks for the Solution of Partial Differential Equations

Speaker: Dr. Cosmin Anitescu, Bauhaus-Universität Weimar

Time: Wednesday 2020.10.28, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

Machine learning and methods based on artificial neural networks have become increasingly common in a variety of topics for areas such as image processing, voice recognition, and object detection. The success in these areas has also led to optimized hardware and software solutions for efficiently training large neural networks and solving previously intractable problems. There is also a great deal of interest in using these techniques for solving complex engineering problems.

In this talk, I will give a brief overview of some algorithms for solving partial differential equations using artificial neural networks, particularly with regard to dealing with the boundary conditions. I will also discuss some possibilities for adaptively choosing the training points and possibilities for further improvements in the efficiency and reliability of neural network-based PDE solvers.

Additional material:

(1) An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications https://doi.org/10.1016/j.cma.2019.112790 or https://arxiv.org/abs/1908.10407

 

 

2 Ph.D. positions in Computational Mechanics in Luxembourg

The computational mechanic’s group of Prof. Stéphane Bordas (www.legato‐team.eu, www.uni.lu ) is searching for two Ph.D. students to work on one of the following projects. Both projects focus on
phase-field damage modeling, rubbery polymers, and experimental validation. All work is carried out in close cooperation with the industrial partner SISTO Armaturen S.A. (www.sisto-aseptic.com,
www.ksb.com ).

Project 1

The project aims to analyze the formation and growth of micro-cracks in rubber due to fatigue loading. Recently published articles on fatigue failure of rubber show that micro-cracks nucleate at
initially present natural flaws. The initial distribution of flaws and the micro-crack growth is investigated with CT-scans and interrupted fatigue tests. These results are used to refine a fatigue
phase-field damage model for this scale. Key points are:

  • Measure the initial flaw size with CT-scans for various process parameter and compounds
  • Interrupted fatigue tests with CT-scans to study the micro-crack growth
  • Fatigue tests with samples with artificially introduced flaws
  • Refine a fatigue phase-field damage model for micro-scale simulations
  • Implementation of a fatigue phase-field fatigue model
  • Parameter calibration and validation of the numerical model with experimental data

Project 2

This project focuses on the thermal aging of rubber. An Arrhenius law is often applied to the test data to predict behavior at lower temperatures. However, this implies that the degradation mechanism at
the highest temperature is the same as at the lower temperatures. A new thermo-chemical model should be developed to study the degradation of material properties over the entire temperature
range. This model is combined with a recently published fatigue phase-field damage model to study the influence of aging on the failure of rubber parts. Key points are:

  • Experimental study of thermal aging taking into account the influence of temperature,
    media, and sample thickness
  • Development of a numerical model to predict the influence of aging on the material
    properties of rubber
  • Couple the new model to a fatigue phase-field damage model
  • Parameter calibration and validation of the numerical model with experimental data

Offer

Four years of funding for the Ph.D. student is provided with a competitive salary. Funds to attend conferences and summer schools are available. Each student will be employed by the university, but
she/he will also spend a significant amount of time in the company.

Candidate profile

Aspiring researchers interested in a Ph.D. topic with strong industrial relevance are encouraged to apply. Only those who hold an MSc degree or will hold one in the near future will be considered. We
are specifically looking for those who hold an MSc degree in Engineering. A background in and affinity with some of the following fields is required:

  • Material/constitutive modeling and structural mechanics
  • Finite element analysis
  • Experimental work
  • Scientific computing (numerical integration, optimization, etc.)
  • Some form of programming (MATLAB, Python, C++, FORTRAN, etc.)

Application

Send one combined email with your application letter and CV to all of the following email addresses:

  • stephane.bordas@gmail.com (Prof. Stéphane Bordas)
  • pascal.loew@ksb.com (Dr. Pascal J. Loew)