We recently proposed a method that uses distance fields to exactly impose boundary conditions in physics-informed neural networks (PINN). This contribution is available as an arXiv preprint.
Dear colleagues,
Our work on 'Multiscale analysis of elastic waves in soft materials: From molecular chain networks to fiber composites' is published in International Journal of Mechanical Sciences.
Please read here: https://doi.org/10.1016/j.ijmecsci.2021.106433
In this paper, we have examined:
To students and junior researchers,
Together with Stephane Bordas (Luxembourg University) and Alban de Vaucorbeil (Deakin University, Australia), we have assembled a PDF (https://imechanica.org/files/how-to-write-paper_29_Nov_2021.pdf) outlining our our approach to writing articles on the field of computational mechanics. Also discussed are softwares used. We hope that it will be of help to students and junior researchers.
Dr Liya Zhao from the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney, Australia, is seeking PhD students to work on projects related to the following topics. Full scholarship will be provided (AUD 28,092/year).
• Small-scale energy harvesting: harnessing renewable energy from vibrations, human motion, wind flows, ocean wave, etc.; developing efficiency enhancement innovations
• Smart structures and systems for vibration/noise suppression (metastructures, adaptive structures with composite smart materials)
Our presentations at the School on Advanced Topics in Computational Mechanics (part of the UK Association of Computational Mechanics 2021 Conference) are now available on the MoFEM YouTube channel (link).
1. Introduction (Lukasz Kaczmarczyk): Sustainable development of research code for complex engineering problems video link
Dear iMechanica colleagues, I am pleased to share with you our newest paper on qauntitative prediction of rapid solidification. S. Kavousi, B. Novak, D. Moldovan, and M. Asle Zaeem. Quantitative prediction of rapid solidification by integrated atomistic and phase-field modeling. Acta Materialia 211 (2021) 116885 (12 pages).
Abstarct
Our team led by Professor Tomasz Wierzbicki at MIT Mechanical Engineering is looking for a highly motivated Postdoctoral Associate in the area of machine learning for solid-state batteries. The candidate is expected to develop machine-learning-based computational tools for the characterization of the interfacial failure in Li-metal all-solid-state batteries. Candidates who have experience in physics-informed machine learning, computational and solid mechanics, multiphysics modeling, and all-solid-state batteries are encouraged to apply by sending a CV to Dr.
Dear colleagues,
We invite you to see the preprint of our new paper "Nonlinear statistical mechanics drives intrinsic electrostriction and volumetric torque in polymer networks" that will appear in Physical Review E. Here we use a nonlinear statistical mechanics approach to the electroelasticity of dielectric polymer chains and obtain a two-way coupling between chain deformation and dielectric response. This two-way coupling leads to electrically induced stresses and volumetric torques within an elastomer network which can be leveraged to develop higher efficiency soft actuators, electroactive materials, and novel electromechanical mechanisms. (https://doi.org/10.1103/PhysRevE.103.042504).
