Second-order methods techniques are widely used in the field of optimization and have been recently applied to neural networks with impressive results. In this seminar, I will present two applications of second-order methods for deep learning.
Delays in freight rail intermodal operations generate negative impacts on the railway industry, so identifying the causes associated to these delays is vital to mitigate operational risks. In this seminar, we will present a set of Machine Learning models that were trained to predict the delays in freight rail intermodal operations, and then the most suitable model was used to explore the significant predictors which cause those delays, using data from the National Railway Company of Luxembourg that connects Bettembourg with several EU countries.
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
Second-order optimization techniques have been proven successful, but cannot be fully leveraged for neural networks due to infeasibility of hessian calculations. I will discuss an approximated way of computing the hessian for neural networks, which emerges from a careful analysis of the hessian chain rule and activation functions.
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.
E Podcast vum Lëtzebuerger Journal an Zesummenaarbecht mam Fonds National de la Recherche. De Max Kasel trëfft sech reegelméisseg mat Fuerscher*innen zu Lëtzebuerg a schwätzt mat hinnen iwwert hiert Liewen an hier Passioun fir d’Wëssenschaft. “Mäin Element” weist de Mënsch hannert der Fuerschung.
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.
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.
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.
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