Dear Colleagues,
I am writing to share an article titled, "Machine-learned prediction of the electronic fields in a crystal", co-authored by Ying-Shi Teh, Professor Kaushik Bhattacharya and myself. The article is published in the journal Mechanics of Materials. Link to the article: https://doi.org/10.1016/j.mechmat.2021.104070
Second nearest neighbor modified embedded-atom method (2NN-MEAM) interatomic potentials are developed for binary aluminum (Al) alloys applicable from room temperature to the melting point. The binary alloys studied in this work are Al-Cu, Al-Fe and Al-Ni. Sensitivity and uncertainty analyses are performed on potential parameters based on the perturbation approach.
Link to the paper - PINN-Poroelasticity
If you are interested in physics informed neural networks (PINNs) and coupled single and multiphase flow in porous media, please check out our work below:
- We find it challenging to solve coupled poroelasticity relations using PINNs (data-free).
EML Webinar (Season 2) on 13 October 2021 will be given by Alain Goriely on dynamics of active filaments. Discussion leader:Krishna Garikipati, University of Michigan
Time: 10 am Boston, 3 pm London, 10 pm Beijing on 13 October 2021
Zoom Link: https://ter.ps/EMLWebinarS2
Mechanobiology and Mechanomedicine: tuning the tension in the life
Baohua Ji (bhji [at] zju.edu.cn)
Department of Engineering Mechanics, Zhejiang University, Hangzhou 310027
Abstract
Our views on how one can leverage machine learning techniques to help constitutive modeling of composite materials are summarized in this paper
Xin Liu, Su Tian, Fei Tao and Wenbin Yu, A review of artificial neural networks in the constitutive modeling of composite materials, Composites Part B, vol. 224, 2021,109152.
https://www.sciencedirect.com/science/article/pii/S1359836821005321.
FYI, this is a recent review article titled, “Physics of surface vibrational resonances: pillared phononic crystals, metamaterials, and metasurfaces,” which has recently appeared in Reports on Progress in Physics.
Rep. Prog. Phys. 84, 086502, 2021
https://iopscience.iop.org/article/10.1088/1361-6633/abdab8
Dynamic Systems and Intelligent Machines (DSIM) Lab at the San Diego State University has an open PhD position in the areas of perception, planning, and control of autonomous systems. Highly motivated applicants with prior experience in robotics, control, and machine learning and strong programming skills and mathematical background are encouraged to apply. Applicants should have B.Sc. and M.Sc. degrees in Computer Science, Mechanical Engineering, or a closely related field.
Dear iMechanicians. I hope that you find the below work interesting - we have developed the first phase field formulation for hydrogen assisted fatigue. Fatigue crack nucleation and growth is predicted for arbitrary geometries, loading histories, and H contents. Virtual S–N curves are also obtained. You can check it here: https://doi.org/10.1016/j.ijfatigue.2021.106521
I have a request to make to physicists: Would they be willing to provide some informal feedback on my new approach to QM?
Update (2021.09.21 15:57 IST): There were unusually many blog hits for the document. ... I do like the work getting noticed, but still, I guess, a clarification is in order:
