Journal articles: 'Gradient-Enhanced'

1

van Zijl, Peter C., and Ralph E. Hurd. "Gradient enhanced spectroscopy." Journal of Magnetic Resonance 213, no. 2 (December 2011): 474–76. http://dx.doi.org/10.1016/j.jmr.2011.08.018.

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2

Hurd, Ralph E. "Gradient-enhanced spectroscopy." Journal of Magnetic Resonance 213, no. 2 (December 2011): 467–73. http://dx.doi.org/10.1016/j.jmr.2011.09.005.

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3

Hurd, Ralph E. "Gradient-enhanced spectroscopy." Journal of Magnetic Resonance (1969) 87, no. 2 (April 1990): 422–28. http://dx.doi.org/10.1016/0022-2364(90)90021-z.

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4

Alfaraj, Mohammed, Yuchun Wang, and Yi Luo. "Enhanced isotropic gradient operator." Geophysical Prospecting 62, no. 3 (March 4, 2014): 507–17. http://dx.doi.org/10.1111/1365-2478.12106.

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5

Moonen, Chrit T. W., Peter Van Gelderen, Geerten W. Vuister, and Peter C. M. Van Zijl. "Gradient-enhanced exchange spectroscopy." Journal of Magnetic Resonance (1969) 97, no. 2 (April 1992): 419–25. http://dx.doi.org/10.1016/0022-2364(92)90327-4.

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6

Gangl, Markus, and Helmut Ritsch. "Cavity-enhanced polarization gradient cooling." Journal of Physics B: Atomic, Molecular and Optical Physics 35, no. 22 (November 4, 2002): 4565–82. http://dx.doi.org/10.1088/0953-4075/35/22/301.

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7

Marro, Kenneth I., Donghoon Lee, and Outi M. Hyyti. "Gradient-enhanced FAWSETS perfusion measurements." Journal of Magnetic Resonance 175, no. 2 (August 2005): 185–92. http://dx.doi.org/10.1016/j.jmr.2005.04.002.

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8

Poh, L. H., and S. Swaddiwudhipong. "Gradient-enhanced softening material models." International Journal of Plasticity 25, no. 11 (November 2009): 2094–121. http://dx.doi.org/10.1016/j.ijplas.2009.01.003.

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9

Parella, T., F. Sanchezferrando, and A. Virgili. "Selective Gradient-Enhanced Inverse Experiments." Journal of Magnetic Resonance, Series A 112, no. 1 (January 1995): 106–8. http://dx.doi.org/10.1006/jmra.1995.1016.

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10

Roumestand, Christian, Pierre Mutzenhardt, Corinne Delay, and Daniel Canet. "Gradient-Enhanced Band-Filtering Experiments." Magnetic Resonance in Chemistry 34, no. 10 (October 1996): 807–14. http://dx.doi.org/10.1002/(sici)1097-458x(199610)34:10<807::aid-omr975>3.0.co;2-9.

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11

SUN, Linjun, Weijun LI, Xin NING, Liping ZHANG, Xiaoli DONG, and Wei HE. "Gradient-Enhanced Softmax for Face Recognition." IEICE Transactions on Information and Systems E103.D, no. 5 (May 1, 2020): 1185–89. http://dx.doi.org/10.1587/transinf.2019edl8103.

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12

Vuister, Geerten W., Rolf Boelens, Robert Kaptein, Maurits Burgering, and Peter C. M. van Zijl. "Gradient-enhanced 3D NOESY-HMQC spectroscopy." Journal of Biomolecular NMR 2, no. 3 (May 1992): 301–5. http://dx.doi.org/10.1007/bf01875323.

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13

Kövér, Katalin E., Dušan Uhrı́n, and Victor J. Hruby. "Gradient- and Sensitivity-Enhanced TOCSY Experiments." Journal of Magnetic Resonance 130, no. 2 (February 1998): 162–68. http://dx.doi.org/10.1006/jmre.1997.1309.

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14

Gerig, J. T. "Gradient-enhanced proton-fluorine NOE experiments." Magnetic Resonance in Chemistry 37, no. 9 (September 1999): 647–52. http://dx.doi.org/10.1002/(sici)1097-458x(199909)37:9<647::aid-mrc520>3.0.co;2-n.

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15

Ulaganathan, Selvakumar, Ivo Couckuyt, Tom Dhaene, Joris Degroote, and Eric Laermans. "Performance study of gradient-enhanced Kriging." Engineering with Computers 32, no. 1 (February 19, 2015): 15–34. http://dx.doi.org/10.1007/s00366-015-0397-y.

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16

Laurenceau, J., M. Meaux, M. Montagnac, and P. Sagaut. "Comparison of Gradient-Based and Gradient-Enhanced Response-Surface-Based Optimizers." AIAA Journal 48, no. 5 (May 2010): 981–94. http://dx.doi.org/10.2514/1.45331.

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17

Yan, Ming, Jianxi Yang, Cen Chen, Joey Tianyi Zhou, Yi Pan, and Zeng Zeng. "Enhanced gradient learning for deep neural networks." IET Image Processing 16, no. 2 (November 9, 2021): 365–77. http://dx.doi.org/10.1049/ipr2.12353.

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18

Lockwood, Brian A., and Mihai Anitescu. "Gradient-Enhanced Universal Kriging for Uncertainty Propagation." Nuclear Science and Engineering 170, no. 2 (February 2012): 168–95. http://dx.doi.org/10.13182/nse10-86.

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19

Simone, Angelo. "Explicit and implicit gradient-enhanced damage models." Revue Européenne de Génie Civil 11, no. 7-8 (August 2007): 1023–44. http://dx.doi.org/10.1080/17747120.2007.9692975.

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20

de Borst, R., A. Benallal, and O. M. Heeres. "A Gradient-Enhanced Damage Approach to Fracture." Le Journal de Physique IV 06, no. C6 (October 1996): C6–491—C6–502. http://dx.doi.org/10.1051/jp4:1996649.

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21

Manzari, Majid T., and Karma Yonten. "C1finite element analysis in gradient enhanced continua." Mathematical and Computer Modelling 57, no. 9-10 (May 2013): 2519–31. http://dx.doi.org/10.1016/j.mcm.2013.01.003.

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22

Shane, Erica S., John L. Anderson, and Michael M. Domach. "Enhanced protein diffusion in a solvent gradient." Industrial & Engineering Chemistry Research 29, no. 2 (February 1990): 309–12. http://dx.doi.org/10.1021/ie00098a024.

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23

Isaksson, P., and R. Hägglund. "Crack-tip fields in gradient enhanced elasticity." Engineering Fracture Mechanics 97 (January 2013): 186–92. http://dx.doi.org/10.1016/j.engfracmech.2012.11.011.

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24

Martínez-Pañeda, Emilio, Sandra Fuentes-Alonso, and Covadonga Betegón. "Gradient-enhanced statistical analysis of cleavage fracture." European Journal of Mechanics - A/Solids 77 (September 2019): 103785. http://dx.doi.org/10.1016/j.euromechsol.2019.05.002.

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25

Floros, Dimosthenis, Fredrik Larsson, and Kenneth Runesson. "On configurational forces for gradient-enhanced inelasticity." Computational Mechanics 61, no. 4 (August 19, 2017): 409–32. http://dx.doi.org/10.1007/s00466-017-1460-x.

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26

Simone, Angelo. "Explicit and implicit gradient-enhanced damage models." Revue européenne de génie civil 11, no. 7-8 (October 1, 2007): 1023–44. http://dx.doi.org/10.3166/regc.11.1023-1044.

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27

Cho, KyungHyun, Tapani Raiko, and Alexander Ilin. "Enhanced Gradient for Training Restricted Boltzmann Machines." Neural Computation 25, no. 3 (March 2013): 805–31. http://dx.doi.org/10.1162/neco_a_00397.

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Restricted Boltzmann machines (RBMs) are often used as building blocks in greedy learning of deep networks. However, training this simple model can be laborious. Traditional learning algorithms often converge only with the right choice of metaparameters that specify, for example, learning rate scheduling and the scale of the initial weights. They are also sensitive to specific data representation. An equivalent RBM can be obtained by flipping some bits and changing the weights and biases accordingly, but traditional learning rules are not invariant to such transformations. Without careful tuning of these training settings, traditional algorithms can easily get stuck or even diverge. In this letter, we present an enhanced gradient that is derived to be invariant to bit-flipping transformations. We experimentally show that the enhanced gradient yields more stable training of RBMs both when used with a fixed learning rate and an adaptive one.

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28

Chu, Jun, Jia Luo, and Lu Leng. "Non-local Dehazing enhanced by color gradient." Multimedia Tools and Applications 78, no. 5 (February 11, 2018): 5701–13. http://dx.doi.org/10.1007/s11042-018-5673-6.

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29

Peerlings, R. H. J., R. de Borst, W. A. M. Brekelmans, and M. G. D. Geers. "Gradient-enhanced damage modelling of concrete fracture." Mechanics of Cohesive-frictional Materials 3, no. 4 (October 1998): 323–42. http://dx.doi.org/10.1002/(sici)1099-1484(1998100)3:4<323::aid-cfm51>3.0.co;2-z.

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30

PEERLINGS, R. H. J., R. DE BORST, W. A. M. BREKELMANS, and J. H. P. DE VREE. "GRADIENT ENHANCED DAMAGE FOR QUASI-BRITTLE MATERIALS." International Journal for Numerical Methods in Engineering 39, no. 19 (October 15, 1996): 3391–403. http://dx.doi.org/10.1002/(sici)1097-0207(19961015)39:19<3391::aid-nme7>3.0.co;2-d.

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31

Wang, Dong, Huan Zhang, Jing Guo, Beichen Cheng, Yuan Cao, Shengjun Lu, Ning Zhao, and Jian Xu. "Biomimetic Gradient Polymers with Enhanced Damping Capacities." Macromolecular Rapid Communications 37, no. 7 (January 18, 2016): 655–61. http://dx.doi.org/10.1002/marc.201500637.

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32

Bouhlel, Mohamed A., and Joaquim R. R. A. Martins. "Gradient-enhanced kriging for high-dimensional problems." Engineering with Computers 35, no. 1 (February 26, 2018): 157–73. http://dx.doi.org/10.1007/s00366-018-0590-x.

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33

Bouwer, Johann M., Daniel N. Wilke, and Schalk Kok. "Spatio-Temporal Gradient Enhanced Surrogate Modeling Strategies." Mathematical and Computational Applications 28, no. 2 (April 8, 2023): 57. http://dx.doi.org/10.3390/mca28020057.

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This research compares the performance of space-time surrogate models (STSMs) and network surrogate models (NSMs). Specifically, when the system response varies over time (or pseudo-time), the surrogates must predict the system response. A surrogate model is used to approximate the response of computationally expensive spatial and temporal fields resulting from some computational mechanics simulations. Within a design context, a surrogate takes a vector of design variables that describe a current design and returns an approximation of the design’s response through a pseudo-time variable. To compare various radial basis function (RBF) surrogate modeling approaches, the prediction of a load displacement path of a snap-through structure is used as an example numerical problem. This work specifically considers the scenario where analytical sensitivities are available directly from the computational mechanics’ solver and therefore gradient enhanced surrogates are constructed. In addition, the gradients are used to perform a domain transformation preprocessing step to construct surrogate models in a more isotropic domain, which is conducive to RBFs. This work demonstrates that although the gradient-based domain transformation scheme offers a significant improvement to the performance of the space-time surrogate models (STSMs), the network surrogate model (NSM) is far more robust. This research offers explanations for the improved performance of NSMs over STSMs and recommends future research to improve the performance of STSMs.

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34

Kang, Shinseong, and Kyunghoon Lee. "Application of Gradient-Enhanced Kriging to Aerodynamic Coefficients Modeling With Physical Gradient Information." Journal of the Korean Society for Aeronautical & Space Sciences 48, no. 3 (March 31, 2020): 175–85. http://dx.doi.org/10.5139/jksas.2020.48.3.175.

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35

An, Xinlai, Weikang Bao, Zuhe Zhang, Zhouwen Jiang, Shengyun Yuan, Zesheng You, and Yong Zhang. "Gradient Enhanced Strain Hardening and Tensile Deformability in a Gradient-Nanostructured Ni Alloy." Nanomaterials 11, no. 9 (September 18, 2021): 2437. http://dx.doi.org/10.3390/nano11092437.

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Gradient-nanostructured material is an emerging category of material with spatial gradients in microstructural features. The incompatibility between gradient nanostructures (GNS) in the surface layer and coarse-grained (CG) core and their roles in extra strengthening and strain hardening have been well elucidated. Nevertheless, whether similar mechanisms exist within the GNS is not clear yet. Here, interactions between nanostructured layers constituting the GNS in a Ni alloy processed by surface mechanical rolling treatment were investigated by performing unique microtension tests on the whole GNS and three subdivided nanostructured layers at specific depths, respectively. The isolated nanograined layer at the topmost surface shows the highest strength but a brittle nature. With increasing depths, isolated layers exhibit lower strength but enhanced tensile plasticity. The GNS sample’s behavior complied more with the soft isolated layer at the inner side of GNS. Furthermore, an extra strain hardening was found in the GNS sample, leading to a greater uniform elongation (>3%) as compared to all of three constituent nanostructured layers. This extra strain hardening could be ascribed to the effects of the strain gradients arising from the incompatibility associated with the depth-dependent mechanical performance of various nanostructured layers.

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36

Chen, G., and G. Baker. "Enhanced Approach to Consistency in Gradient-Dependent Plasticity." Advances in Structural Engineering 7, no. 3 (July 2004): 279–83. http://dx.doi.org/10.1260/136943304323213229.

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37

Titscher, Thomas, Javier Oliver, and Jörg F. Unger. "Implicit–Explicit Integration of Gradient-Enhanced Damage Models." Journal of Engineering Mechanics 145, no. 7 (July 2019): 04019040. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001608.

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38

Laurent, Luc, Rodolphe Le Riche, Bruno Soulier, and Pierre-Alain Boucard. "An Overview of Gradient-Enhanced Metamodels with Applications." Archives of Computational Methods in Engineering 26, no. 1 (July 17, 2017): 61–106. http://dx.doi.org/10.1007/s11831-017-9226-3.

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39

Li, Xikui, Junbo Zhang, and Xue Zhang. "Micro-macro homogenization of gradient-enhanced Cosserat media." European Journal of Mechanics - A/Solids 30, no. 3 (May 2011): 362–72. http://dx.doi.org/10.1016/j.euromechsol.2010.10.008.

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40

Ulaganathan, Selvakumar, Ivo Couckuyt, Francesco Ferranti, Eric Laermans, and Tom Dhaene. "Performance study of multi-fidelity gradient enhanced kriging." Structural and Multidisciplinary Optimization 51, no. 5 (November 26, 2014): 1017–33. http://dx.doi.org/10.1007/s00158-014-1192-x.

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41

Jiang, Ting, and XiaoJian Zhou. "Gradient/Hessian-enhanced least square support vector regression." Information Processing Letters 134 (June 2018): 1–8. http://dx.doi.org/10.1016/j.ipl.2018.01.014.

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42

Tyburn, Jean-Max, Ian M. Brereton, and David M. Doddrell. "Coherence selection in gradient-enhanced, heteronuclear correlation spectroscopy." Journal of Magnetic Resonance (1969) 97, no. 2 (April 1992): 305–12. http://dx.doi.org/10.1016/0022-2364(92)90315-x.

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43

Li, Gang, and Fuh-Gwo Yuan. "Gradient enhanced damage sizing for structural health management." Smart Materials and Structures 24, no. 2 (January 23, 2015): 025036. http://dx.doi.org/10.1088/0964-1726/24/2/025036.

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44

Chen, Tinggui, Junrui Jiao, and Dejie Yu. "Enhanced broadband acoustic sensing in gradient coiled metamaterials." Journal of Physics D: Applied Physics 54, no. 8 (December 8, 2020): 085501. http://dx.doi.org/10.1088/1361-6463/abc6d7.

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45

Zhang, Chun-Lei, Hui-Jing Du, Jian-Zhuo Zhu, Tian-Fu Xu, and Xiao-Yong Fang. "Enhanced Photovoltaic Properties of Gradient Doping Solar Cells." Chinese Physics Letters 29, no. 12 (December 2012): 127305. http://dx.doi.org/10.1088/0256-307x/29/12/127305.

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46

Zuiderweg, Erik R. P., and Aikaterini Rousaki. "Gradient-enhanced TROSY described with Cartesian product operators." Concepts in Magnetic Resonance Part A 38A, no. 6 (November 2011): 280–88. http://dx.doi.org/10.1002/cmr.a.20228.

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47

Xu, Yanjie, and Leong Hien Poh. "Localizing gradient‐enhanced Rousselier model for ductile fracture." International Journal for Numerical Methods in Engineering 119, no. 9 (April 15, 2019): 826–51. http://dx.doi.org/10.1002/nme.6074.

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48

Geers, M. G. D., R. L. J. M. Ubachs, and R. A. B. Engelen. "Strongly non-local gradient-enhanced finite strain elastoplasticity." International Journal for Numerical Methods in Engineering 56, no. 14 (2003): 2039–68. http://dx.doi.org/10.1002/nme.654.

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49

Peerlings, R. H. J., W. A. M. Brekelmans, R. de Borst, and M. G. D. Geers. "Gradient-enhanced damage modelling of high-cycle fatigue." International Journal for Numerical Methods in Engineering 49, no. 12 (2000): 1547–69. http://dx.doi.org/10.1002/1097-0207(20001230)49:12<1547::aid-nme16>3.0.co;2-d.

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50

Hurd, R. E., A. Deese, M. O'Neil Johnson, S. Sukumar, and P. C. M. van Zijl. "Impact of Differential Linearity in Gradient-Enhanced NMR." Journal of Magnetic Resonance, Series A 119, no. 2 (April 1996): 285–88. http://dx.doi.org/10.1006/jmra.1996.0089.

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Journal articles on the topic 'Gradient-Enhanced'

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