APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020;
Denver, Colorado
Session Index
Session R20: Data Science III: Deep Learning
Focus
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Sponsoring Units: GDS
Chair: Emine Kucukbenli, Harvard University
Room: 301
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R20.00001: Deep Learning-enabled Computational Microscopy and Sensing
Invited Speaker:
Aydogan Ozcan
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R20.00002: Exploring Organic Ferroelectrics Using Data-driven Approaches
Ayana Ghosh, Nicholas Lubbers, Serge M Nakhmanson, Jian-Xin Zhu
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R20.00003: Deep Learning Model for Finding New Superconductors
Tomohiko Konno, Hodaka Kurokawa, Fuyuki Nabeshima, Yuki Sakishita, Ryo Ogawa, Iwao Hosako, Atsutaka Maeda
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R20.00004: Deep Learning for Energetic Materials: Predicting Material Properties from Electronic Structure using Convolutional Neural Networks
Alex Casey, Brian Barnes, Ilias Bilionis, Steven F. Son
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R20.00005: Optimization of Molecular Characteristic using Continuous Representation of Molecules by Variational Autoencoder with Discriminator
Kyosuke Sato, Kenji Tsuruta
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R20.00006: An Initial Design-based Deep Learning Procedure for the Optimization of High Dimensional ReaxFF Parameters
MERT SENGUL, Yao Song, Linglin He, Ying Hung, Tirthankar Dasgupta, Adri C.T. van Duin
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R20.00007: Feature Extraction Using Semi-Supervised Deep Learning.
Muammar El Khatib, Wibe A De Jong
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R20.00008: Unsupervised feature extraction in simple physical models through mutual information maximization
Leopoldo Sarra, Florian Marquardt
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R20.00009: Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Samuel Kim, Peter Lu, Michael Gilbert, Srijon Mukherjee, Li Jing, Vladimir Čeperić, Marin Soljacic
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R20.00010: Rapid machine learning-based solutions of partial differential equations on complex domains.
Vikas Dwivedi, Balaji Srinivasan
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R20.00011: Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models
Joshua Chang, Shashaank Vattikuti, Carson C Chow
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R20.00012: Turbulence-generating networks
Armando Garcia, Rao Gudimetla, Jorge Munoz
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R20.00013: SignalTrain: Modeling Time-dependent Nonlinear Signal Processing Effects Using Deep Neural Networks
William Mitchell, Scott Hawley
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