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Deep Neural Networks Motivated by Partial Differential Equations

12 April 2018

Papers citing "Deep Neural Networks Motivated by Partial Differential Equations"

50 / 228 papers shown

Title
Characteristic Neural Ordinary Differential Equations Xingzi Xu Ali Hasan Khalil Elkhalil Jie Ding Vahid Tarokh BDL 76 3 0 25 Nov 2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 63 98 0 02 Nov 2021
Sinkformers: Transformers with Doubly Stochastic Attention Michael E. Sander Pierre Ablin Mathieu Blondel Gabriel Peyré 87 85 0 22 Oct 2021
GCCN: Global Context Convolutional Network Ali Hamdi Flora D. Salim D. Kim 73 1 0 22 Oct 2021
Signature-Graph Networks Ali Hamdi Flora D. Salim D. Kim Xiaojun Chang 48 1 0 22 Oct 2021
Solving Image PDEs with a Shallow Network Pascal Getreuer P. Milanfar Xiyang Luo 68 1 0 15 Oct 2021
Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation Peirong Liu Rui Wang Xuefei Cao Yipin Zhou Ashish Shah Ser-Nam Lim DiffM 66 3 0 09 Oct 2021
Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation Wrik Mallik R. Jaiman J. Jelovica AI4CE 57 3 0 08 Oct 2021
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems Subhabrata Dutta Tanya Gautam Soumen Chakrabarti Tanmoy Chakraborty 116 18 0 30 Sep 2021
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks Elizabeth Newman Julianne Chung Matthias Chung Lars Ruthotto 63 6 0 28 Sep 2021
Short-term traffic prediction using physics-aware neural networks M. Pereira Annika Lang Balázs Kulcsár 81 22 0 21 Sep 2021
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations Ido Ben-Yair Gil Ben Shalom Moshe Eliasof Eran Treister MQ 83 5 0 31 Aug 2021
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods Tobias Alt Karl Schrader Joachim Weickert Pascal Peter M. Augustin 71 4 0 31 Aug 2021
m-RevNet: Deep Reversible Neural Networks with Momentum Duo Li Shangqi Gao 83 5 0 12 Aug 2021
Deep Microlocal Reconstruction for Limited-Angle Tomography Héctor Andrade-Loarca Gitta Kutyniok Ozan Oktem P. Petersen 64 8 0 12 Aug 2021
Deep Neural Networks and PIDE discretizations B. Bohn M. Griebel D. Kannan 47 1 0 05 Aug 2021
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations Moshe Eliasof E. Haber Eran Treister GNN AI4CE 102 131 0 04 Aug 2021
Connections between Numerical Algorithms for PDEs and Neural Networks Tobias Alt Karl Schrader M. Augustin Pascal Peter Joachim Weickert PINN 68 22 0 30 Jul 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks Zhiqian Chen Fanglan Chen Lei Zhang Taoran Ji Kaiqun Fu Liang Zhao Feng Chen Lingfei Wu Charu C. Aggarwal Chang-Tien Lu 160 20 0 21 Jul 2021
Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network Yiran Wang Zhen Li 28 2 0 19 Jul 2021
Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs S. Dutta Peter Rivera-Casillas Orie M. Cecil Matthew W. Farthing E. Perracchione M. Putti AI4CE 51 7 0 06 Jul 2021
Cell-average based neural network method for hyperbolic and parabolic partial differential equations Changxin Qiu Jue Yan 56 10 0 02 Jul 2021
Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment Hao Huang Boulbaba Ben Amor Xichan Lin Fan Zhu Yi Fang AI4TS MedIm 37 6 0 22 Jun 2021
Stateful ODE-Nets using Basis Function Expansions A. Queiruga N. Benjamin Erichson Liam Hodgkinson Michael W. Mahoney 54 16 0 21 Jun 2021
Steerable Partial Differential Operators for Equivariant Neural Networks Erik Jenner Maurice Weiler 80 29 0 18 Jun 2021
Encoding physics to learn reaction-diffusion processes Chengping Rao Pu Ren Qi Wang O. Buyukozturk Haoqin Sun Yang Liu PINN AI4CE DiffM 105 97 0 09 Jun 2021
Hard Encoding of Physics for Learning Spatiotemporal Dynamics Chengping Rao Hao Sun Yang Liu PINN AI4CE 77 12 0 02 May 2021
Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics S. Dutta Peter Rivera-Casillas Matthew W. Farthing AI4CE 47 13 0 22 Apr 2021
PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs Zhengyang Shen Tiancheng Shen Zhouchen Lin Jinwen Ma 51 21 0 08 Apr 2021
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation Bei Li Quan Du Tao Zhou Shuhan Zhou Xin Zeng Tong Xiao Jingbo Zhu 63 23 0 06 Apr 2021
Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows Thomas S. Brown Harbir Antil R. Löhner F. Togashi Deepanshu Verma AI4CE 35 15 0 01 Apr 2021
Translating Numerical Concepts for PDEs into Neural Architectures Tobias Alt Pascal Peter Joachim Weickert Karl Schrader 67 6 0 29 Mar 2021
Rethinking ResNets: Improved Stacking Strategies With High Order Schemes Zhengbo Luo Zitang Sun Weilian Zhou Zizhang Wu Sei-ichiro Kamata 34 16 0 28 Mar 2021
JFB: Jacobian-Free Backpropagation for Implicit Networks Samy Wu Fung Howard Heaton Qiuwei Li Daniel McKenzie Stanley Osher W. Yin FedML 127 85 0 23 Mar 2021
Meta-Solver for Neural Ordinary Differential Equations Julia Gusak A. Katrutsa Talgat Daulbaev A. Cichocki Ivan Oseledets 65 2 0 15 Mar 2021
ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks Boulbaba Ben Amor Sylvain Arguillere Ling Shao 59 30 0 16 Feb 2021
Momentum Residual Neural Networks Michael E. Sander Pierre Ablin Mathieu Blondel Gabriel Peyré 88 58 0 15 Feb 2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs Juntang Zhuang Nicha Dvornek S. Tatikonda James S. Duncan 101 52 0 09 Feb 2021
Mimetic Neural Networks: A unified framework for Protein Design and Folding Moshe Eliasof Tue Boesen E. Haber C. Keasar Eran Treister AI4CE 68 12 0 07 Feb 2021
Accuracy and Architecture Studies of Residual Neural Network solving Ordinary Differential Equations Changxin Qiu Aaron Bendickson Joshua Kalyanapu Jue Yan 20 1 0 10 Jan 2021
Residual networks classify inputs based on their neural transient dynamics F. Lagzi 37 0 0 08 Jan 2021
Hybrid FEM-NN models: Combining artificial neural networks with the finite element method Sebastian K. Mitusch S. Funke M. Kuchta AI4CE 126 99 0 04 Jan 2021
Towards Natural Robustness Against Adversarial Examples Haoyu Chu Shikui Wei Yao-Min Zhao AAML 21 1 0 04 Dec 2020
Kinetics-Informed Neural Networks G. S. Gusmão Adhika Retnanto Shashwati C. da Cunha A. Medford 28 29 0 30 Nov 2020
Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems Kookjin Lee E. Parish 71 68 0 28 Oct 2020
Improving seasonal forecast using probabilistic deep learning B. Pan G. Anderson André Goncalves Donald D. Lucas C. Bonfils Jiwoo Lee BDL AI4Cl 118 33 0 27 Oct 2020
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods Byungjoo Kim Bryce Chudomelka Jinyoung Park Jaewoo Kang Youngjoon Hong Hyunwoo J. Kim AAML 36 6 0 20 Oct 2020
A Principle of Least Action for the Training of Neural Networks Skander Karkar Ibrahhim Ayed Emmanuel de Bézenac Patrick Gallinari AI4CE 55 10 0 17 Sep 2020
Deep Learning in Photoacoustic Tomography: Current approaches and future directions A. Hauptmann B. Cox 142 131 0 16 Sep 2020
Large-time asymptotics in deep learning Carlos Esteve Borjan Geshkovski Dario Pighin Enrique Zuazua 371 34 0 06 Aug 2020