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DeePN2: A deep learning-based non-Newtonian hydrodynamic model

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arXiv 2021年

作者： Fang, Lidong Ge, Pei Zhang, Lei Weinan, E. Lei, Huan Department of Computational Mathematics Science and Engineering Michigan State University MI48824 United States School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing100080 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States Department of Statistics and Probability Michigan State University MI48824 United States

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN2, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN2 using more complex micro-structural models. We show that DeePN2 can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention. © 2021, CC BY.

关键词： Non Newtonian flow

Google COVID-19 community mobility reports: Insights from multi-criteria decision making

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arXiv 2020年

作者： da Silva, Gabriela Cavalcante Oliveira, Sabrina Wanner, Elizabeth F. Bezerra, Leonardo C.T. Brazil Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Brazil Computer Engineering Dept. CEFET-MG Brazil Computer Science Group Aston University United Kingdom

Social distancing (SD) has been critical in the fight against the novel coronavirus disease (COVID-19). To aid SD monitoring, many technology companies have made available mobility data, the most prominent example being the community mobility reports (CMR) provided by Google. Given the wide range of research fields that have been drawing insights from CMR data, there has been a rising concern for methodological discussion on how to use them. Indeed, Google recently released their own guidelines, concerning the nature of the place categories and the need for calibrating regional values. In this work, we discuss how measures developed in the field of multi-criteria decision making (MCDM) might benefit researchers analyzing this data. Concretely, we discuss how Pareto dominance and performance measures adopted in MCDM enable the mobility evaluation for (i) multiple categories for a given time period and (ii) multiple categories over multiple time periods. We empirically demonstrate these approaches conducting both a region- and country-level analysis, comparing some of the most relevant outbreak examples from different continents. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Nonuniform 3D finite difference elastic wave simulation on staggered grids

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arXiv 2020年

作者： Gao, Longfei Ghattas, Omar Keyes, David Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Oden Institute for Computational Engineering and Sciences Jackson School of Geosciences Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States Applied Mathematics and Computational Science Program Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We present an approach to simulate the 3D isotropic elastic wave propagation using nonuniform finite difference discretization on staggered grids. Specifically, we consider simulation domains composed of layers of uniform grids with different grid spacings, separated by nonconforming interfaces. We demonstrate that this layer-wise finite difference discretization has the potential to significantly reduce the simulation cost, compared to its fully uniform counterpart. Stability of such a discretization is achieved by using specially designed difference operators, which are variants of the standard difference operators with adaptations near boundaries or interfaces, and penalty terms, which are appended to the discretized wave system to weakly impose boundary or interface conditions. Combined with specially designed interpolation operators, the discretized wave system is shown to preserve the energy conserving property of the continuous elastic wave equation, and a fortiori ensure the stability of the simulation. Numerical examples are presented to demonstrate the efficacy of the proposed simulation approach. © 2020, CC BY.

关键词： Elastic waves

Emergence of new optical resonances in single-layer transition metal dichalcogenides with atomic-size phase patterns

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arXiv 2022年

作者： Woods, John M. Chand, Saroj B. Mejia, Enrique Taniguchi, Takashi Watanabe, Kenji Flick, Johannes Grosso, Gabriele Photonics Initiative Advanced Science Research Center City University of New York New YorkNY United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Center for Computational Quantum Physics Flatiron Institute New YorkNY10010 United States Department of Physics City College of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Atomic-scale control of light-matter interactions represent the ultimate frontier for many applications in photonics and quantum technology. Two-dimensional semiconductors, including transition metal dichalcogenides, are a promising platform to achieve such control due to the combination of an atomically thin geometry and convenient photophysical properties. Here, we demonstrate that a variety of durable polymorphic structures can be combined to generate additional optical resonances beyond the standard excitons. We theoretically predict and experimentally show that atomic-sized patches of 1T phase within the 1H matrix form unique electronic bands that give rise to new and robust optical resonances with strong absorption, circularly polarized emission and long radiative lifetime. The atomic manipulation of two-dimensional semiconductors opens unexplored scenarios for light harvesting devices and exciton-based photonics. Copyright © 2022, The Authors. All rights reserved.

关键词： Excitons

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease ( Oct, 10.1038/s43587-021-00122-7, 2021)

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NATURE AGING

NATURE AGING 2021年第12期1卷 1202-1202页

作者： Taubes, Alice Nova, Phil Zalocusky, Kelly A. Kosti, Idit Bicak, Mesude Zilberter, Misha Y. Hao, Yanxia Yoon, Seo Yeon Oskotsky, Tomiko Pineda, Silvia Chen, Bin Jones, Emily Aery A. Choudhary, Krishna Grone, Brian Balestra, Maureen E. Chaudhry, Fayzan Paranjpe, Ishan De Freitas, Jessica Koutsodendris, Nicole Chen, Nuo Wang, Celine Chang, William An, Alice Glicksberg, Benjamin S. Sirota, Marina Huang, Yadong Gladstone Institute of Neurological Disease Gladstone Institutes San Francisco CA USA Biomedical Sciences Graduate Program University of California San Francisco CA USA Gladstone Center for Translational Advancement Gladstone Institutes San Francisco CA USA Department of Neurology University of California San Francisco CA USA Development and Stem Cell Biology Graduate Program University of California San Francisco CA USA Department of Pathology University of California San Francisco CA USA Bakar Computational Health Sciences Institute University of California San Francisco CA USA Department of Pediatrics University of California San Francisco CA USA Department of Surgery University of California San Francisco CA USA Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA Gladstone Institute of Data Science and Biotechnology Gladstone Institutes San Francisco CA USA

The evident genetic, pathological and clinical heterogeneity of Alzheimer’s disease (AD) poses challenges for traditional drug development. We conducted a computational drug-repurposing screen for drugs to treat apolipoprotein E4 (APOE4)-related AD. We first establishedAPOEgenotype-dependent transcriptomic signatures of AD by analyzing publicly available human brain databases. We then queried these signatures against the Connectivity Map database, which contains transcriptomic perturbations of more than 1,300 drugs, to identify those that best reverseAPOEgenotype-specific AD signatures. Bumetanide was identified as a top drug forAPOE4-related AD. Treatment ofAPOE4-knock-in mice without or with amyloid β (Aβ) accumulation using bumetanide rescued electrophysiological, pathological or cognitive deficits. Single-nucleus RNA sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed inAPOE4induced pluripotent stem cell (iPSC)-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 years in two electronic health record databases, suggesting the effectiveness of bumetanide in preventing AD.

关键词： OBSTRUCTIVE SLEEP-APNEA APOLIPOPROTEIN-E APOE EPSILON-4 EXPRESSION RISK E4 AGE ASSOCIATION METABOLISM MECHANISMS

Deep learning of accurate force field of ferroelectric HfO2

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Physical Review B 2021年第2期103卷 024108-024108页

作者： Jing Wu Yuzhi Zhang Linfeng Zhang Shi Liu Fudan University Shanghai 200433 China School of Science Westlake University Hangzhou Zhejiang 310024 China Institute of Natural Sciences Westlake Institute for Advanced Study Hangzhou Zhejiang 310024 China Yuanpei College Peking University Beijing 100871 China Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Key Laboratory for Quantum Materials of Zhejiang Province Hangzhou Zhejiang 310024 China

The discovery of ferroelectricity in HfO2-based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density nonvolatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at finite temperatures. Molecular dynamics is an ideal technique for investigating dynamical processes on large length and time scales, though its applications to new materials are often hindered by the limited availability and accuracy of classical force fields. Here we present a deep neural network–based interatomic force field of HfO2 learned from ab initio data using a concurrent learning procedure. The model potential is able to predict structural properties such as elastic constants, equation of states, phonon dispersion relationships, and phase transition barriers of various hafnia polymorphs with accuracy comparable with density functional theory calculations. The validity of this model potential is further confirmed by the reproduction of experimental sequences of temperature-driven ferroelectric-paraelectric phase transitions of HfO2 with isobaric-isothermal ensemble molecular dynamics simulations. We suggest a general approach to extend the model potential of HfO2 to related material systems including dopants and defects.

关键词： Interatomic & molecular potentials Structural properties Ferroelectrics Artificial neural networks Molecular dynamics

Detecting Abnormal PCG Signals and Extracting Cardiac Information Employing Deep Learning and the Shannon Energy Envelope

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Detecting Abnormal PCG Signals and Extracting Cardiac Inform...

IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

ISBN: (数字)9781728188201

ISBN: (纸本)9781728188218

Phonocardiogram (PCG) is a computerized system that represents the heart sound recording. PCG reflects the acoustic behavior of the heart graphically through intensity, frequency, time duration, and other valuable information. An acoustic signal like PCG provides supplemental diagnostic information to Electrocardiogram (ECG) by extracting cardiac information from the heart sound those cannot be identified by hearing the heart sound. PCG is an objective and standard evaluation technique that can record the heart sound continuously for a long period of time and can also overcome the human hearing limitation [1] [2]. So, PCG plays a vital role to examine the heart sound as well as cardiac abnormalities which improves the overall diagnosis efficiency. We proposed a PCG classification method using the deep convolutional neural network (CNN). Our proposed method is not only able to classify PCG signals using CNN but can also segment PCG signals using the Shannon energy envelope method. This signal processing technique provides significant information regarding the heart condition that helps to detect heart diseases in the primary phase.

关键词： Heart Time-frequency analysis Auditory system Signal processing Acoustics Data mining Phonocardiography

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

High-temperature mid-IR absorption and reaction kinetics of 2-methyl-1,3-dioxolane: An experimental and theoretical study

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Journal of Photochemistry and Photobiology 2023年 13卷

作者： Adil, Mohammad Giri, Binod Raj Mai, Tam V.-T. Szőri, Milán Huynh, Lam K. Farooq, Aamir Mechanical Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia KAUST Clean Combustion Research Center King Abdullah University of Science and Technology Saudi Arabia Molecular Science and Nano-Materials Lab Institute for Computational Science and Technology SBI Building Quang Trung Software City Tan Chanh Hiep Ward District 12 Ho Chi Minh Viet Nam University of Science Vietnam National University – HCMC 227 Nguyen Van Cu Ward 4 District 5 Ho Chi Minh Viet Nam Vietnam National University – HCMC Quarter 6 Linh Trung Ward Thu Duc Ho Chi Minh Viet Nam Institute of Chemistry University of Miskolc Egyetemváros A/2 Miskolc H-3515 Hungary School of Chemical and Environmental Engineering International University Quarter 6 Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam

This work reports the mid-IR spectroscopy and reaction kinetics of 2-methyl-1,3-dioxolane (2M13DO). We carried out spectroscopic measurements to deduce temperature-dependent absorption cross-sections of 2M13DO over a broad wavelength range of 8.4–10.5 μm (950–1190 cm⁻¹). For these measurements, we employed a rapidly tuning MIRcat-QT™ laser that can be operated either at a fixed wavelength or scanned mode over wide wavelength regions. By operating the laser at a fixed wavelength, we monitored the decay of 2M13DO behind reflected shock waves over T₅ = 1050–1400 K and P₅ = 0.7 and 2.6 bar. Our measured concentration time-histories of 2M13DO allowed us to directly extract the overall rate coefficients for the unimolecular decomposition of 2M13DO using the first-order rate law. We did not observe any pressure dependence in the measured rate coefficients, indicating that the reaction is close to the high-pressure limit. By employing the W1U composite method, we explored the important pyrolysis reaction pathways of 2M13DO in the reactive potential energy surface. Three important reaction channels, namely, 2M13DO → CH₂[dbnd]CHOCH₂CH₂OH (IM1), 2M13DO → 2CH₃CHO (P3), 2M13DO → CH₃ + 1,3-dioxolan-2-yl (P4) were identified. Below 700 K, IM1 forming channel is dominant, whereas CH₃CHO formation is dominant under our experimental conditions. Above 1500 K, the radical forming channel (CH₃+P4) takes over other channels. At higher temperatures, the contribution of the radical forming channel continually increases, accounting for ∼ 99% at 2000 K. We used the stochastic RRKM-ME model to predict the pressure and temperature dependence of the rate coefficients, k(T, P), and time-resolved species profiles. Our theory showed excellent agreement with the measured rate coefficients. These are the first direct determination of the rate coefficients of the unimolecular decomposition of

关键词： 2-Methyl-1,3-dioxolane Absorption spectra High-temperature Pyrolysis RRKM-ME calculations Shock tube