A5061668124- Model Reduction and Neural Networks
- Elasticity and Material Modeling
- Cardiac Valve Diseases and Treatments
- Chalcogenide Semiconductor Thin Films
- Numerical methods for differential equations
- 2D Materials and Applications
- Machine Learning in Materials Science
- Polymer composites and self-healing
- Advanced Thermoelectric Materials and Devices
- Non-Destructive Testing Techniques
- Seismic Imaging and Inversion Techniques
- Electromagnetic Scattering and Analysis
- Coronary Interventions and Diagnostics
- Fuel Cells and Related Materials
- Composite Material Mechanics
- Advanced Mathematical Modeling in Engineering
- Semiconductor Quantum Structures and Devices
- Pediatric health and respiratory diseases
- Advanced Numerical Methods in Computational Mathematics
- Thermal properties of materials
- Customer churn and segmentation
- Imbalanced Data Classification Techniques
- Cellular and Composite Structures
- Microwave Imaging and Scattering Analysis
- Microfluidic and Capillary Electrophoresis Applications
Nanjing Medical University
2021-2024
Brown University
2022-2024
John Brown University
2023-2024
Beijing Hua Xin Hospital
2024
National University of Defense Technology
2019-2023
University of Tasmania
2023
Jiangsu Province Hospital
2022
Tianjin University
2022
Harvard University Press
2018
Tsinghua University
2018
Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, nonlinear deformation. Here, we present general framework based on physics-informed neural networks for identifying geometric parameters. By using mesh-free method, parameterize the geometry of differentiable trainable method that can identify multiple structural features. We validate this approach...
Polyacrylamide hydrogels are highly stretchable and nearly elastic. Their stress-stretch curves exhibit small hysteresis, change negligibly after many loading cycles. is used extensively in applications, the primary network for types of tough hydrogels. Recent experiments have shown that polyacrylamide susceptible to fatigue fracture, but available data limited. Here we study fracture various water contents. We form polymer networks all samples under same conditions, then obtain 96, 87, 78,...
Aortic dissection progresses mainly via delamination of the medial layer wall. Notwithstanding complexity this process, insight has been gleaned by studying in vitro and silico progression driven quasi-static pressurization intramural space fluid injection, which demonstrates that differential propensity along aorta can be affected spatial distributions structurally significant interlamellar struts connect adjacent elastic lamellae. In particular, diverse histological microstructures may...
We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials. focus on the problem with a background in elasticity imaging, where one seeks to identify mechanical properties soft tissue based full-field displacement measurements under quasi-static loading. In our model, we two independent neural networks, approximating solution corresponding forward problem, and other unknown material parameter field. As proof concept, validate model...
Abstract The InAs/AlAs superlattice structures hold significant potential for application in low-noise avalanche photodetectors. With their performance practical applications linked to fundamental physical property of carrier relaxation time, this paper investigates the times superlattices across various mono-layers, temperatures, and concentrations. Our investigation indicates that span several tens picoseconds, confirming high-quality interfaces do not significantly reduce manner defect...
We extend a recently proposed machine-learning-based iterative solver, i.e. the hybrid transferable solver (HINTS), to solve scattering problem described by Helmholtz equation in an exterior domain with complex absorbing boundary condition. The HINTS method combines neural operators (NOs) standard solvers, e.g. Jacobi and Gauss-Seidel (GS), achieve better performance leveraging spectral bias of networks. In HINTS, some iterations conventional are replaced inferences pre-trained NO. this...
Abstract Recently, intriguing physical properties have been unraveled in anisotropic layered semiconductors, which the in-plane electronic band structure anisotropy often originates from low crystallographic symmetry and thus a thickness-independent character emerges. Here, we apply high-resolution angle-resolved photoemission spectroscopy to directly image energy bands monoclinic gallium telluride (GaTe). Our first-principles calculations reveal of GaTe measured experimentally is dominated...
Thermoelectric materials, based on photo-thermoelectric effect (PTE), may be promising in photo-detection because of their self-power, extremely broad-band, and free cryogenic attachments. Up to now, the performance PTE is mainly optimized through enhancement extrinsic absorption such as using optical metamaterials. Instead, we here improve materials engineering, accordingly systematically investigated both P- N-type SnSe crystals with different carrier concentrations (1017–1019 cm−3)....
Soft network materials constructed with horseshoe microstructures represent a class of bio-inspired synthetic that can be tailored precisely to match the nonlinear, J-shaped, stress–strain curves human skins. Under large level stretching, nonlinear deformations associated drastic changes microstructure geometries lead an evident mechanical anisotropy, even for honeycomb and triangular lattices sixfold rotational symmetry. Such anisotropic responses are essential certain targeted applications...
Machine learning (ML) is emerging as a transformative tool for the design of architected materials, offering properties that far surpass those achievable through lab-based trial-and-error methods. However, major challenge in current inverse strategies their reliance on extensive computational and/or experimental datasets, which becomes particularly problematic designing micro-scale stochastic materials exhibit nonlinear mechanical behaviors. Here, we introduce new end-to-end scientific ML...
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present novel genotype-to-biomechanical phenotype neural network (G2Φnet) characterizing classifying biomechanical properties which serve as important...
Iterative solvers of linear systems are a key component for the numerical solutions partial differential equations (PDEs). While there have been intensive studies through past decades on classical methods such as Jacobi, Gauss-Seidel, conjugate gradient, multigrid and their more advanced variants, is still pressing need to develop faster, robust reliable solvers. Based recent advances in scientific deep learning operator regression, we propose HINTS, hybrid, iterative, numerical,...
Nonequilibrium molecular dynamics is widely used to calculate the thermal conductivity of various materials, but influence temperature gradient has received limited attention within current research studies. The purpose this article explore discrepancy between intrinsic and extrinsic conductivities under different gradients, which can be considered as external fields. analyses phonon density states have shown that plays a role in field, larger activates more low-frequency vibrational modes,...