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Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals

Submitted by mohsenzaeem on

Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification ofstochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations ofmetal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyseswhere thousands of random realizations are necessary.

A brief review of modeling of composite structures

Submitted by Wenbin Yu on

This paper provides a brief review on modeling of composite structures. Composite structures in this paper refer to any structure featuring anisotropy and heterogeneity, including but not limited to their traditional meaning of composite laminates made of unidirectional fiber-reinforced composites. Common methods used in modeling of composite structures, including the axiomatic method, the formal asymptotic method, and the variational asymptotic method, are illustrated in deriving the classical lamination theory for the composite laminated plates to see their commonalities and differences.

EML Webinar Young Researchers Forum by Xueju Wang, on 16 January 2024: Morphing Materials and Multifunctional Structures/Electronics for Intelligent Systems

Submitted by Zheng Jia on

EML Webinar (Young Researchers Forum) on 16 January 2024 will be given by Xueju Wang at University of Connecticut via Zoom meeting

Title: Morphing Materials and Multifunctional Structures/Electronics for Intelligent Systems

Discussion leader: Teng Zhang, Syracuse University

Time: 9:30 am Boston, 2:30 pm London, 3:30 pm Paris, 10:30 pm Beijing on Tuesday, 16 January 2024

Thermal fluctuations (eventually) unfold nanoscale origami

Submitted by matthew.grasinger on

We investigate the mechanics and stability of a nanoscale origami crease via a combination of equilibrium and nonequilibrium statistical mechanics. We identify an entropic torque on nanoscale origami creases, and find stability properties have a nontrivial dependence on bending stiffness, radii of curvature of its creases, ambient temperature, its thickness, and its interfacial energy.

International Summer School "Mechanics of active soft materials: experiments, theory, numerics, and applications"

Submitted by giulia scalet on

Glad to share that the University of Pavia, together with Politecnico di Milano, Technion - Israel Institute of Technology and University of Bologna, organizes the International Summer School “Mechanics of active soft materials: experiments, theory, numerics, and applications” within the Lake Como School of Advanced Studies, from 1st to 5th July 2024 at Villa del Grumello (Como, Italy). https://star.lakecomoschool.org/

On laminated structures under flexure

Submitted by Lorenzo Bardella on

If you design laminated structures, such as sandwich panels, you might be interested in knowing that the through-the-thickness normal stress, properly disregarded in homogeneous structures, may play a fundamental role in triggering delamination.

Abstract call for Thematic Session 'SM12 - Plasticity, viscoplasticity and creep' - ICTAM2024 (Daegu, South Korea, Aug 25-30, 2024)

Submitted by Lorenzo Bardella on

Dear Colleagues, 

within the 26th International Congress of Theoretical and Applied Mechanics (ICTAM2024) to be held in Daegu, South Korea, 25-30 Aug 2024, Henrik M. Jensen (Aarhus University, Denmark) and myself are organising the Thematic Session 'SM12 - Plasticity, viscoplasticity and creep'. 

We would like to invite you to contribute to this Thematic Session. 

The Extended Abstract Submission is open until January 15, 2024.

Best regards,

Journal Club for January 2024: Machine Learning in Experimental Solid Mechanics: Recent Advances, Challenges, and Opportunities

Submitted by Hanxun Jin on

Hanxun Jin (a,b), Horacio D. Espinosa (b)
a Division of Engineering and Applied Science, California Institute of Technology
b Department of Mechanical Engineering, Northwestern University

In recent years, Machine Learning (ML) has become increasingly prominent in Solid Mechanics. Its diverse applications include extracting unknown material parameters, developing surrogate models for constitutive modeling, advancing multiscale modeling, and designing architected materials. In this Journal Club, we will focus our discussion on the recent advances and challenges of ML when experimental data is involved. With broad community interest, as reflected by the increasing number of publications in this field, we have recently published a review article in Applied Mechanics Reviews titled “Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review”. Moreover, a recent insightful paper from Prof. Sam Daly’s group also discussed some perspectives in this field. In this Journal Club, we would like to introduce and share insights into this exciting field.