We are offering an internship position for 4 to 6 months at the University of Luxembourg, in the Legato team led by Prof. Stéphane P.A. Bordas (FLSW). The subject deals with implementing the IsoGeometric Analysis in the SOFA Framework. Join a dynamic team and an open-source consortium! More details are provided in the attached document.
GPT models (Generative Pre-trained Transformer) have increasingly become a popular choice by researchers and practitioners. Their success is mostly due to the technology’s ability to move beyond single word predictions. Indeed, unlike in traditional neural network language models, GPT generates text by looking at the entirety of the input text. Thus, rather than determining relevance sequentially by looking at the most recent segment of input, GPT models determine each word’s relevance selectively. However, if on the one hand this ability allows the machine to ‘learn’ faster, the datasets used for training have to be fed as one single document, meaning that all metadata information is inevitably lost (e.g., date, authors, original source). Moreover, as GPT models are trained on crawled, English web material from 2016, these models are not only ignorant of the world prior to this date, but they also express the language as used exclusively by English-speaking users (mostly white, young males). They also expect data pristine in quality, in the sense that these models have been trained on digitally-born material which do not present the typical problems of digitized, historical content (e.g., OCR mistakes, unusual fonts). Although a powerful technology, these issues seriously hinder its application for humanistic enquiry, particularly historical. In this presentation, I discuss these and other problematic aspects of GPT and I present the specific challenges I encountered while working on a historical archive of Italian American immigrant newspapers.
In the last decades, artificial neural networks (ANNs) have drawn academy and industry attention for their ability to represent and solve complex problems. ANNs use algorithms based on stochastic gradient descent (SGD) to learn data patterns from training examples, which tends to be time-consuming. Researchers are studying how to distribute this computation across multiple GPUs to reduce training time. Modern implementations rely on synchronously scaling up resources or asynchronously scaling them out using a centralized communication network. However, both of these approaches have communication bottlenecks, which may impair their scaling time. In this research, we create TensAIR, a framework that scales out the training of sparse ANNs models in an asynchronously and decentralized manner. Due to the commutative properties of SGD updates, we linearly scaled out the number of gradients computed per second with minimal impairment on the convergence of the models – relative to the models’ sparseness. These results indicate that TensAIR enables the training of sparse neural networks in significantly less time. We conjecture that this article may inspire further studies on the usage of sparse ANNs on time-sensitive scenarios like online machine learning, which until now would not be considered feasible.
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.
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
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.
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.
* Title: Mechanical Behavior of Cells and Biomaterials
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.
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.
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.
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.
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.
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.
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.
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