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ردیف | عنوان | نوع |
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1 |
A mesh-free method for interface problems using the deep learning approach
روشی بدون مش برای مشکلات رابط با استفاده از روش یادگیری عمیق-2020 In this paper, we propose a mesh-free method to solve interface problems using the deep learning approach. Two types of PDEs are considered. The first one is an elliptic PDE with a discontinuous and high-contrast coefficient. While the second one is a linear elasticity equation with discontinuous stress tensor. In both cases, we represent the solutions of the PDEs using the deep neural networks (DNNs) and formulate the PDEs into variational problems, which can be solved via the deep learning approach. To deal with inhomogeneous boundary conditions, we use a shallow neural network to approximate the boundary conditions. Instead of using an adaptive mesh refinement method or specially designed basis functions or numerical schemes to compute the PDE solutions, the proposed method has the advantages that it is easy to implement and is mesh-free. Finally, we present numerical results to demonstrate the accuracy and efficiency of the proposed method for interface problems. Keywords: Deep learning | Variational problems | Mesh-free method | Linear elasticity | High-contrast | Interface problems |
مقاله انگلیسی |
2 |
Development of reversible and durable thermochromic phase-change microcapsules for real-time indication of thermal energy storage and management
توسعه میکروکپسولهای تغییر فاز گرمایشی برگشت پذیر و بادوام برای نشان دادن زمان واقعی ذخیره انرژی و مدیریت انرژی-2020 We reported a design of novel thermochromic phase-change microcapsules (TCMs) with a sandwich-structured
shell for reversible and durable indication of thermal energy storage and management in real-time. Two types of
TCMs with red and blue color indicators were successfully constructed by fabricating a silica base shell onto the
n-docosane core, followed by formation of a thermochromic indication layer and a polymeric protective layer,
and their multilayered configuration and well-defined core-shell structure were confirmed by microstructural
investigation and chemical composition analysis. These two types of TCMs not only showed an outstanding
latent heat-storage/release capability with a high capacity over 150 J/g, but also exhibited a good shape stability,
high thermal stability and excellent phase-change reversibility and durability. The optimum operation
conditions for thermal energy charge/discharge were also determined by nonisothermal and isothermal differential
scanning calorimetric analyses. Most of all, the two types of TCMs presented an entirely reversible
thermochromic behavior individually with high-contrast red and blue color indications for the phase-change
state of n-docosane core. Both of them exhibited high reversibility and long cycle life in thermochromic indication,
which meets the design requirements for durable indication of latent heat storage and thermal management
in real-time. In the light of an innovative configuration of sandwich-structured shell and a smart combination of latent heat-storage and thermochromic functions, the TCMs designed by this study has a great
potential for applications in smart fibers and textiles, wearable electric devices, energy-saving buildings, temperature-
sensitive medical system, safety clothing, smart windows, aerospace engineering and many more. Keywords: Phase-change microcapsules | Sandwich-structured configuration | Reversible thermochromic behavior | Thermal energy storage | Reliability and durability |
مقاله انگلیسی |
3 |
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
ایجاد پیوندهای محلی سازی ساختار و خاصیت برای تغییر شکل الاستیک کامپوزیت های کنتراست بالا سه بعدی با استفاده از روشهای یادگیری عمیق-2019 Data-driven methods are attracting growing attention in the field of materials science. In particular, it is
now becoming clear that machine learning approaches offer a unique avenue for successfully mining
practically useful process-structure-property (PSP) linkages from a variety of materials data. Most previous
efforts in this direction have relied on feature design (i.e., the identification of the salient features
of the material microstructure to be included in the PSP linkages). However due to the rich complexity of
features in most heterogeneous materials systems, it has been difficult to identify a set of consistent
features that are transferable from one material system to another. With flexible architecture and
remarkable learning capability, the emergent deep learning approaches offer a new path forward that
circumvents the feature design step. In this work, we demonstrate the implementation of a deep learning
feature-engineering-free approach to the prediction of the microscale elastic strain field in a given threedimensional
voxel-based microstructure of a high-contrast two-phase composite. The results show that
deep learning approaches can implicitly learn salient information about local neighborhood details, and
significantly outperform state-of-the-art methods. Keywords: Materials informatics | Convolutional neural networks | Deep learning | Localization | Structure-property linkages |
مقاله انگلیسی |
4 |
A mesh-free method for interface problems using the deep learning approach
روش بدون مش برای مسائل واسط با استفاده از روش یادگیری عمیق-2019 In this paper, we propose a mesh-free method to solve interface problems using the deep
7 learning approach. Two types of PDEs are considered. The first one is an elliptic PDE with
8 a discontinuous and high-contrast coefficient. While the second one is a linear elasticity equa9
tion with discontinuous stress tensor. In both cases, we represent the solutions of the PDEs
10 using the deep neural networks (DNNs) and formulate the PDEs into variational problems,
11 which can be solved via the deep learning approach. To deal with inhomogeneous boundary
12 conditions, we use a shallow neural network to approximate the boundary conditions. Instead
13 of using an adaptive mesh refinement method or specially designed basis functions or numer14
ical schemes to compute the PDE solutions, the proposed method has the advantages that it
15 is easy to implement and is mesh-free. Finally, we present numerical results to demonstrate
16 the accuracy and efficiency of the proposed method for interface problems.
17 AMS subject classification: 35J20, 35R05, 65N30, 68T99, 74B05. Keywords: Deep learning | variational problems | mesh-free method | linear elasticity | 19 high-contrast | interface problems |
مقاله انگلیسی |
5 |
Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups
معادله کنتراست ژنتیکی انتزاعی نشان می دهد که ژن های جدید ژن خاص برای زیرگروه های اوتیسم هستند-2018 Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and
gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene
groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this
end, we developed a method to associate gene combinations with groups with shared autism traits, targeting
genetic elements that distinguish patient populations with opposing phenotypes. Our computational method
prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight
potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited
Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating
the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant
combinations are tested for significant subgroup associations. We apply this method by contrasting autism
subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes
to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with sub
groups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups,
but our informatics method can explore other meaningful divisions of autism patients, and can further be applied
to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer’s
disease.
Keywords: Data mining ، Autistic disorder ، Genetics ، Frequent pattern mining |
مقاله انگلیسی |