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pro vyhledávání: '"Zhai, Hanfeng"'
Autor:
Zhai, Hanfeng
Polycrystal plasticity in metals is characterized by nonlinear behavior and strain hardening, making numerical models computationally intensive. We employ Graph Neural Networks (GNN) to surrogate polycrystal plasticity with complex geometries from Fi
Externí odkaz:
http://arxiv.org/abs/2409.05169
Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algorithms are not as widely applied to materials discovery applications as
Externí odkaz:
http://arxiv.org/abs/2309.02646
Autor:
Zhai, Hanfeng, Yeo, Jingjie
Publikováno v:
Journal of the Mechanical Behavior of Biomedical Materials 147, (2023): 106127
Biofilm growth and transport in confined systems frequently occur in natural and engineered systems. Designing customizable engineered porous materials for controllable biofilm transportation properties could significantly improve the rapid utilizati
Externí odkaz:
http://arxiv.org/abs/2305.08574
Autor:
Zhai, Hanfeng, Yeo, Jingjie
Publikováno v:
International Journal of Applied Mechanics, 2023
The thermo-mechanical coupling mechanism of graphene fracture under thermal gradients possesses rich applications whereas is hard to study due to its coupled non-equilibrium nature. We employ non-equilibrium molecular dynamics to study the fracture o
Externí odkaz:
http://arxiv.org/abs/2212.07897
Autor:
Zhai, Hanfeng, Yeo, Jingjie
Publikováno v:
ACS Biomater. Sci. Eng. 2022
Biofilms pose significant problems for engineers in diverse fields, such as marine science, bioenergy, and biomedicine, where effective biofilm control is a long-term goal. The adhesion and surface mechanics of biofilms play crucial roles in generati
Externí odkaz:
http://arxiv.org/abs/2209.00055
Autor:
Zhai, Hanfeng, Sands, Timothy
Publikováno v:
Sensors 2022, 22, 6362
Controlling nonlinear dynamics arise in various engineering fields. We present efforts to model the forced van der Pol system control using physics-informed neural networks (PINN) compared to benchmark methods, including idealized nonlinear feedforwa
Externí odkaz:
http://arxiv.org/abs/2206.08831
Autor:
Zhai, Hanfeng
We review the computation models for biofilm and bacteria cells, providing perspectives on biofilm's various properties and potential serving as engineering living materials (ELMs), considering the omnipresence of such biological matter. The minirevi
Externí odkaz:
http://arxiv.org/abs/2206.03895
Autor:
Zhai, Hanfeng, Sands, Timothy
Publikováno v:
Mathematics 2022, 10(3), 453
Controlling nonlinear dynamics is a long-standing problem in engineering. Harnessing known physical information to accelerate or constrain stochastic learning pursues a new paradigm of scientific machine learning. By linearizing nonlinear systems, tr
Externí odkaz:
http://arxiv.org/abs/2112.14707
Publikováno v:
AIP Advances 12, 035153 (2022)
Micro-bubbles and bubbly flows are widely observed and applied in chemical engineering, medicine, involves deformation, rupture, and collision of bubbles, phase mixture, etc. We study bubble dynamics by setting up two numerical simulation cases: bubb
Externí odkaz:
http://arxiv.org/abs/2105.07179
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