GRACM 2021: Fracture across space and time: a journey through multiscale methods, model order reduction and machine learning

GRACM 2021: Fracture across space and time: a journey through multiscale methods, model order reduction and machine learning

Professor Bordas will give a talk the 07/07/21 from 2:45 pm to 3:15 pm at the 10th GRACM.

To register : click here

For the full program: click here

Abstract

Fracture across space and time: a journey through multiscale methods, model order reduction and machine learning

We review and connect in this paper two approaches to solve multi-scale fracture problems. Machine learning and model order reduction on the one hand and multi-scale methods on the other hand. 

We explain as didactically as possible how material complexity has led to the need for acceleration methods and discuss advances in model selection and error estimation for such problems. 

We show how model reduction/machine learning and standard multiscale methods both fails when dealing with localization problems occurring in fracture mechanics. 

We conclude by discussing the possibility of digital twins and make a parallel with medical simulation. 

 

Literature


A computational library for multiscale modeling of material failure
H Talebi, M Silani, SPA Bordas, P Kerfriden, T Rabczuk
Computational Mechanics 53 (5), 1047-1071

Bridging proper orthogonal decomposition methods and augmented Newton–Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems
P Kerfriden, P Gosselet, S Adhikari, SPA Bordas
Computer Methods in Applied Mechanics and Engineering 200 (5-8), 850-866

A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics
P Kerfriden, O Goury, T Rabczuk, SPA Bordas
Computer Methods in Applied Mechanics and Engineering 256, 169-188

Local/global model order reduction strategy for the simulation of quasi-brittle fracture
P Kerfriden, JC Passieux, SPA Bordas
International Journal for Numerical Methods in Engineering 89 (2), 154-179

Molecular Dynamics/XFEM Coupling by a Three Dimensional Extended Bridging Domain with Applications to Dynamic Brittle Fracture
H Talebi, M Silani, S Bordas, P Kerfriden, T Rabczuk
International Journal for Multiscale Computational Engineering 11 (6), 527-541


Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization
O Goury, D Amsallem, SPA Bordas, WK Liu, P Kerfriden
Computational Mechanics 58 (2), 213–234

Quasicontinuum-based multiscale approaches for plate-like beam lattices experiencing in-plane and out-of-plane deformation
LAA Beex, P Kerfriden, T Rabczuk, SPA Bordas
Computer Methods in Applied Mechanics and Engineering 279, 348–378


Statistical extraction of process zones and representative subspaces in fracture of random composite
P Kerfriden, KM Schmidt, T Rabczuk, SPA Bordas
International Journal for Multiscale Computational Engineering 11 (3), 253-287

Guaranteed error bounds in homogenisation: an optimum stochastic approach to preserve the numerical separation of scales
D Alves Paladim, JP Moitinho de Almeida, SPA Bordas, P Kerfriden
International Journal for Numerical Methods in Engineering 110 (2), 103–132

What makes data science different? A discussion involving statistics2. 0 and computational sciences
C Ley, SPA Bordas
International Journal of Data Science and Analytics 6 (3), 167-175

Mathematical modelling and artificial intelligence in Luxembourg: Twenty PhD students to be trained in data-driven modelling
S Bordas, S Natarajan, A Zilian
ERCIM News 115, 39-40

Dr. Katerina E. Aifantis: Mechanical Behavior of Cells and Biomaterials 06/30/21 at 11:00 am

Dr. Katerina E. Aifantis: Mechanical Behavior of Cells and Biomaterials  06/30/21 at 11:00 am

Legato Team (https://legato-team.eu/) has organised a seminar on the topic Mechanical Behavior of Cells and Biomaterials. I am writing this email to invite you all to attend this insightful seminar that will be held on June 30, 2021 at Room No: MNO-E01-0146030 (Maison du Nombre) at 11:00 am (Limited places available due to Covid restrictions). You can also attend the seminar through Webex as well as Youtube live (Links are given below).

The seminar will surely be helpful for all students as well as researchers, thus do participate and gain maximum benefit out of it. 

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Introduction to Speaker:

 

Dr. Katerina E. Aifantis

Associate Professor,

Mechanical and Aerospace Engineering,

University of Florida

 

Dr. Katerina E. Aifantis received her PhD from the University of Groningen in 2005 at the age of 21, becoming the youngest PhD in the Netherlands. After a short post-doctoral period at Harvard/US and at Ecole des Mines of Paris/France, She became the youngest recipient of the European Research Council Starting/ERC Grant at the age of 24, which she carried out at Aristotle University of Thessaloniki and the University of Erlangen-Nuremberg, between 2008-2013. In 2013, she joined the University of Arizona as an Associate Professor, and since 2017 she has been an Associate Professor and Faculty Fellow at the Mechanical and Aerospace Engineering Department of the University of Florida, where she set up the Laboratory of Nanomaterials for Energy and Biological Applications. Her research primarily focuses on using solid mechanics for understanding materials behavior at the nanoscale, such as dislocation-grain boundary and dislocation-graphene interactions. In addition to basic science questions, she uses her theoretical and experimental insight to predict the most promising materials systems that can be used in various applications, ranging from next generation electrodes for Li-ion batteries, to bone regeneration scaffolds and bioacompatible electrodes for deep brain stimulation.

* When: Wednesday, June 30, 2021 
Time: 11:00 a.m. (Paris time)

* Where:

Room No MNO-E01-0146030Maison du Nombre, University of Luxembourg (Belval Campus)  (Limited places available due to Covid restrictions).  

Webex Meet room: https://unilu.webex.com/unilu/j.php?MTID=m1a56ac0cc40f6070f0496603684679c7

Youtube link: https://youtu.be/M8WQFBSJkbs

 

* Title: Mechanical Behavior of Cells and Biomaterials

 

* Abstract:

       The present talk will focus on understanding the mechanical behavior of cells and biomaterials. Tissue regeneration is an area which depends on the ability to develop materials that poses the appropriate combination of microstructure and stiffness that promote cell adhesion and differentiation. Heart patches are a most challenging case, as they need to be seeded with cardiomyocytes and then placed in the infracted area of the heart. New polymer blends of Poly(Glycerol Sebacate) (PGS) prepolymer and Polybutaline Succinate (PBS-DLA) are introduced as possible patches, as they increase the viability of myocytes, depending on their elastic modulus and porosity.

      In the second part, similar fibrous materials will be used to study cancer cell evasion. Collagen co-polymers will be used of different concentrations in order to change the porosity while glutaraldehyde will be employed to change the stiffness. Seeding the different samples with evasive cancer cells, allows to capture the interplay between tissue pore size versus stiffness in promoting cell migration.

       In the third part, similar experimental tools used in determining the mechanical properties of biomaterials, will be used to examine the deformation of cells. Particularly, atomic force microscopy will be employed to capture the effects that the common blood thinning medication, pentoxifylline, has on the elastic modulus of red blood cells. This was the first in vivo experiment of its kind, as the blood samples were taken from human subjects taking this medication. In continuing to a different setup, optical tweezers will be used to show that cells undergo plastic deformation in addition to viscoelastic deformation.

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