25 research outputs found

    Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

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    Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at CMES-Computer Modeling in Engineering & Science

    A static hysteresis model for power ferrites

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    Efficient simulation of coupled circuit-field problems: Generalized falk method

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    Abstract—The proposed generalized Falk (GF) method offers an extremely simple and convenient way to solve coupled circuit-field problems in circuit simulators by transforming the discretized governing-field equations into guaranteed stable-and-passive one-dimensional (1-D) equivalent-circuit systems, which are then combined with the circuit part of the overall coupled problem. More efficient than the traditional Lanczos-type methods, the GF method transforms a general finite-element system represented by a system of full matrices into an identity capacitance (mass) matrix and a tridiagonal conductance (stiffness) matrix. No positive poles are produced; all transformed matrices remain positive definite. The resulting 1-D equivalent-circuit system contains only resistors, capacitors, inductors, and current sources. Several numerical examples are provided. Index Terms—Converters, coordinate transformation, coupled circuit-field problems, electrothermal simulation, insulated gate bipolar transistor (IGBT) device, power electronics, reorthogonalization, stable and passive one-dimensional (1-D) equivalent thermal circuit. I

    Dr. Devendra Garg

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    This paper presents a rational approach to construct thermal circuit networks equivalent to a discretization of the heat equation by the finite element method. Elemental thermal circuit networks are developed, which correspond to the linear and cubic Hermite elements in the 1-D case, to the triangular and rectangular elements in the 2-D case, and to the tetrahedral and cube elements in the 3-D case. These thermal circuit networks are to be 18 Nov 1997, 12:2

    Chemically Modulated Band Gap in Bilayer Graphene Memory Transistors with High On/Off Ratio

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    We report a chemically conjugated bilayer graphene field effect transistor demonstrating a high on/off ratio without significant degradation of the on-current and mobility. This was realized by introducing environmentally stable benzyl viologen as an electron-donating group and atmospheric dopants as an electron-withdrawing group, which were used as dopants for the bottom and top of the bilayer graphene, respectively. A high mobility of ∼3100 cm2 V-1 s-1 with a high on/off ratio of 76.1 was obtained at room temperature without significant degradation of the on-current. This is attributed to low charge scattering due to physisorbed dopants without provoking sp3 structural disorders. By utilizing our band-gap-opened bilayer graphene, excellent nonvolatile memory switching behavior was demonstrated with a clear program/erase state by applying pulse gate bias. The initial program/erase current ratio of ∼34.5 was still retained at higher than 10 even after 104 s. © 2015 American Chemical Society120231sciescopu

    EFFECT OF METAL NANOPARTICLES ON THE GROWTH OF NGOC LINH GINSENG (PANAX VIETNAMENSIS) LATERAL ROOTS CULTURED IN VITRO

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    Ngoc Linh ginseng plays important in pharmaceutical industry because its triterpenoid saponin drugs could improve health and treat many diseases. Metal nanoparticles revealed completely new properties based on specific characteristics (size, distribution and morphology) compared to metal ions or salts, especially, their potential applications in plant tissue culture. Effects of metal nanoparticles (0.5–2.5 mg/L nZnO, 1–3 mg/L nAg, and 1–3 mg/L nCu) in free-hormone-MS medium were studied on the Ngoc Linh ginseng root culture. Results showed that nAg and nCu increased positive effects on the lateral root formation and growth at different concentrations. Except for, the root abnormal anatomical morphology revealed that cell layers of xylem, endodermis and epidermis thicken and darken, or vascular bundle expand, or some black points appeared in root caps and dorsal bundles. The optimal metal nanoparticle for the root growth is nCu; and the highest growth indexes were obtained at 1.5 mg/L nCu with 99.3% lateral root formation). The ginseng root grew at 2.5 mg/L nAg better, but more abnormalities. The inhibition growth and negative impacts on the ginseng root were recorded in the medium containing nZnO (0.5–2.5 mg/L) and the highest metal nanoparticle concentration (above 2.5 mg/L nCu and nAg).Panax vietnamensis (Ngoc Linh ginseng) plays critical roles in pharmaceutical industry because triterpenoid saponins from its roots produce medicine for improving health and treating many diseases. Metal nanoparticles reveal completely new or improved properties based on specific characteristics such as size, distribution and morphology compare to metal ion or salt; and their potential for in vitro plant cultures. Present study investigated the effects of metal nanoparticles including nZnO (0.5-2.5 mg/l), nAg (1-3 mg/l), and nCu (1-3 mg/l) supplemented in free-hormone-MS medium to in vitro Panax vietnamensis lateral root growth. Our results showed that metal nanoparticles have the positive effect on the growth of in vitro P. vietnamensis lateral roots with nAg, nCu, and nZnO. At different concentrations, in vitro P. vietnamensis lateral root growth also has various effects on the growth of lateral roots. In supplemented metal nanoparticle treatments, nCu is the most optimum for in vitro P. vietnamensis lateral root growth; the highest increase was obtained at 1.5 mg/l nCu treatment (99.3% lateral root formation and all root growth indexes are the highest). Besides, 2.5 mg/l nAg is also significantly noticed in ginseng root growth. However, the negative impact on the growth of the in vitro P. vietnamensis lateral roots showed when culture medium contained the highest concentration; such as the root growing inhibition of nCu and nAg above 2.5 mg/l. Especially, this decrease was higher with the application of nZnO0.5-2.5 mg/l (decrease the lateral root number) and 2.5 mg/l (decrease percent of lateral root formation)
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