Institution
Rensselaer Polytechnic Institute
Education•Troy, New York, United States•
About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Finite element method. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.
Papers published on a yearly basis
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University of California, Riverside1, Purdue University2, Pacific Northwest National Laboratory3, Rensselaer Polytechnic Institute4, SUNY Downstate Medical Center5, University of Michigan6, Brown University7, University of Pennsylvania8, University of California, Santa Barbara9, Stanford University10
TL;DR: It is demonstrated that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces.
Abstract: Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.
315 citations
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TL;DR: In this article, the authors describe an experi-mental study that shows by placing the phosphor away from the die, the backscattered photons can be extracted and the energy efficiency can be significantly increased.
Abstract: White light-emitting diode (LED) luminous efficacy must improve significantly if LEDs are to become useful for general lighting. Phosphors commonly used in white LEDs backscatter more than half of the down-converted light, of which a significant portion is eventually lost within the package, thus reducing the overall efficacy. This letter describes an experi-mental study that shows by placing the phosphor away from the die, the backscattered photons can be extracted and the ef-ficacy can be significantly increased. At low currents, the lu-minous efficacy exceeded 80 lm/W. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
314 citations
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TL;DR: In this article, the authors focus on some of the recent achievements of the academic and industrial community in boosting the power densities of Lithium ion batteries through the development of novel nanostructured anode and cathode architectures.
314 citations
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TL;DR: The concentration of yttrium in pelitic garnets as a function of metamorphic grade has been examined in relation to the distribution of xenotime (YPO 4 ) in samples from New England and British Columbia.
Abstract: The concentration of yttrium in pelitic garnets as a function of metamorphic grade has been examined in relation to the distribution of xenotime (YPO 4 ) in samples from New England and British Columbia. Samples with xenotime present only as inclusions in garnet generally possess high-Y cores and concentrations that drop off discontinuously along zoning shoulders of variable width to low-Y outboard regions. Samples with matrix xenotime are restricted to the garnet zone; Y concentration of these garnets generally decreases smoothly from core to rim. Xenotime may also be present in reaction zones around garnet. In xenotime-bearing samples, [Y] G r t is strongly temperature-dependent and ranges from ∼5000 ppm in the garnet zone to ∼150 ppm in the sillimanite zone. Measured yttrium zoning profiles in xenotime-absent samples are reproduced with both Rayleigh fractionation and diffusion models, but P-T histories of the samples examined favor the Rayleigh model, with garnet volume, bulk-rock yttrium, and mode of (Y, HREE) accessory phases controlling the profile shape. High-yttrium annuli in staurolite-zone samples may form by garnet overgrowth of proximal matrix enriched in yttrium due to garnet consumption during discontinuous staurolite-forming reactions. An increase in [Y] G r t and [HREE] G r t in garnet from anatectic samples is related to dissolution of phosphates in vapor-absent, peraluminous melt, with partitioning of highly compatible Y and HREE into garnet grown during anatexis; textural analysis reveals that phosphates are absent from regions of garnet grown in equilibrium with melt. A main result of this study is identification of an intimate coupling between major pelite phases and accessory phases during reaction progress. This coupling is of great advantage in that it may be used to (1) calibrate sensitive geothermometers and geobarometers, (2) identify particular regions of garnet grown in different garnet-producing reactions over a range of grades, and (3) reveal portions of pelite reaction history invisible to major elements.
313 citations
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TL;DR: In this article, the authors report on the contact resistances for pentacene thin film transistors with two different designs: top and bottom contact configurations (referred to as TC and BC TFTs, respectively) for two different contact metals (gold and palladium).
Abstract: We report on the contact resistances for pentacene thin film transistors with two different designs: top and bottom contact configurations (referred to as TC and BC TFTs, respectively) for two different contact metals (gold and palladium). The extraction was done based on the dependencies of the channel resistances on the gate length and gate voltage. The extracted gold TC TFT contact resistance depends on VGS, but shows no dependence on the drain bias. The TC TFT contact resistance is comparable to or exceeds the channel resistance for channels shorter than approximately 10 μm. The contact resistance of BC TFTs depends both on gate and drain bias. We propose a circuit simulating the BC TFT contact resistance and verify the circuit applicability by extracting and comparing the TFT channel resistances at different drain voltages. Our results reveal an important role played by contact resistances and provide an accurate model of the contact phenomena suitable for implementation in Spice or other circuit simulators.
312 citations
Authors
Showing all 19133 results
Name | H-index | Papers | Citations |
---|---|---|---|
Pulickel M. Ajayan | 176 | 1223 | 136241 |
Zhenan Bao | 169 | 865 | 106571 |
Murray F. Brennan | 161 | 925 | 97087 |
Ashok Kumar | 151 | 5654 | 164086 |
Joseph R. Ecker | 148 | 381 | 94860 |
Bruce E. Logan | 140 | 591 | 77351 |
Shih-Fu Chang | 130 | 917 | 72346 |
Michael G. Rossmann | 121 | 594 | 53409 |
Richard P. Van Duyne | 116 | 409 | 79671 |
Michael Lynch | 112 | 422 | 63461 |
Angel Rubio | 110 | 930 | 52731 |
Alan Campbell | 109 | 687 | 53463 |
Boris I. Yakobson | 107 | 443 | 45174 |
O. C. Zienkiewicz | 107 | 455 | 71204 |
John R. Reynolds | 105 | 607 | 50027 |