46 citations · 131 across the 14 of their papers we have counts for
15 papers
Scalable dynamical inference of phase-field fracture from sparse and partial measurements
Hanfeng Zhai, Zisheng Zhang
Evolving crack fields in structural health monitoring and fracture assessment must often be inferred from sparse mechanical measurements rather than dense full-field observations.…
A convolutional neural network surrogate for hierarchical homogenization: fast elastic moduli prediction of digital rocks
Hanfeng Zhai, Rasool Ahmad, Tapan Mukerji +1
Digital rock physics (DRP) aims to estimate effective rock properties (e.g., elastic moduli) directly from 3D micro-CT images. However, direct numerical simulations (DNS) on high-r…
Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
Hanfeng Zhai, Hongtao Qiao, Hassan Mansour +1
We present a scalable, data-driven simulation framework for large-scale heating, ventilation, and air conditioning (HVAC) systems that couples physics-informed neural ordinary diff…
Learning Interatomic Force Coefficients from X-ray Thermal Diffuse Scattering Data
Klara Suchan, Shaswat Mohanty, Hanfeng Zhai +1
We present a fully automated framework for extracting interatomic force constants (IFCs) directly from X-ray thermal diffuse scattering (TDS) data. By formulating scattering intens…
Atomistic and data-driven insights into the local slip resistances in random refractory multi-principal element alloys
Wu-Rong Jian, Arjun S. Kulathuvayal, Hanfeng Zhai +5
Refractory multi-principal element alloys (RMPEAs) have attracted growing interest for their exceptional high-temperature strength, yet their complex compositions hinder a mechanis…
Link Statistics of Dislocation Network during Strain Hardening
Sh. Akhondzadeh, Hanfeng Zhai, Wurong Jian +3
Dislocations are line defects in crystals that multiply and self-organize into a complex network during strain hardening. The length of dislocation links, connecting neighboring no…
Stress Predictions in Polycrystal Plasticity using Graph Neural Networks with Subgraph Training
Hanfeng Zhai
Numerical modeling of polycrystal plasticity is computationally intensive. We employ Graph Neural Networks (GNN) to predict stresses on complex geometries for polycrystal plasticit…
Benchmarking Inverse Optimization Algorithms for Materials Design
Hanfeng Zhai, Hongxia Hao, Jingjie Yeo
Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algori…
Controlling Biofilm Transport with Porous Metamaterials Designed with Bayesian Learning
Hanfeng Zhai, Jingjie Yeo
Biofilm growth and transport in confined systems frequently occur in natural and engineered systems. Designing customizable engineered porous materials for controllable biofilm tra…
Multiscale mechanics of thermal gradient coupled graphene fracture: A molecular dynamics study
Hanfeng Zhai, Jingjie Yeo
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…
Computational design of antimicrobial active surfaces via automated Bayesian optimization
Hanfeng Zhai, Jingjie Yeo
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 adh…
Multiscale Computational Modeling of Biofilm
Hanfeng Zhai
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)…
Comparison of Deep Learning and Deterministic Algorithms for Control Modeling
Hanfeng Zhai, Timothy Sands
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)…
Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning
Hanfeng Zhai, Timothy Sands
Controlling nonlinear dynamics is a long-standing problem in engineering. Harnessing known physical information to accelerate or constrain stochastic learning pursues a new paradig…
BubbleNet: Inferring micro-bubble dynamics with semi-physics-informed deep learning
Hanfeng Zhai, Quan Zhou, Guohui Hu
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 st…