Dr. Lei Wang’s research group at the University of Cincinnati (UC) is seeking two high-motivated and talented PhD students to be appointed as Graduate Assistants to conduct geotechnical engineering research in the Department of Civil and Architectural Engineering and Construction Management, beginning January 2023 or August 2023.
POST-DOCTORAL POSITION WITHIN THE PROJECT
"Physics-informed Artificial Intelligence for Cognitive Twins of Complex Systems"
Fully funded Ph.D. positions in the area of computational mechanics are available in Dr. Yuan’s research group at the Aerospace Engineering Department at Worcester Polytechnic Institute.e
We introduce a generalized methodology to uncover all mechanical couplings in 2D lattice geometries by obtaining the decoupled micropolar elasticity tensor. We also correlate the mechanical couplings with the point groups of 2D lattices by applying the symmetry operation to the decoupled micropolar elasticity tensor. The decoupled micropolar constitutive equation reveals eight mechanical coupling effects in planar solids, four of which are discovered for the first time in the mechanics' community.
The College of Engineering at Boston University has embarked on a bold new strategic plan that will pursue excellence and impact along convergent themes: (1) Intelligent, Autonomous and Secure Systems, (2) Synthetic Biology, Tissue Engineering and Mechanobiology, (3) Energy, Sustainability and Climate, (4) Materials by Design, (5) Photonics and Optical Systems, and (6) Neuroengineering, Neuroinformatics, and Neuroscience.
Join Synopsys Simpleware software, CYBERNET Multiscale.Sim, and Shimadzu Corporation for a webinar on Thursday, October 13th, 2022 on composite material analysis integrated actual testing and CAE.
Learn more and register here: https://register.gotowebinar.com/rt/204553566873541131?source=iMechanica
Solving topology optimization problem is very computationally demanding especially when high resolution results are sought for. In the present work, a problem-independent machine learning (PIML) technique is proposed to reduce the computational time associated with finite element analysis (FEA) which constitutes the main bottleneck of the solution process.
I would like to draw your attention to our recently proposed predictive method based on a semi-empirical model (LEFM) and Neural Network, exploiting the Physics-informed Machine Learning concept. We show how the accuracy of state-of-the-art fatigue predictive models, based on defects present in materials, can be significantly boosted by accounting for additional morphological features via Physics-Informed Machine Learning.
