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The main computational task of Scientific Machine Learning (SciML) is function regression, required both for inputs as well as outputs of a simulation.
Recherches quantitatives sur l’excitation electrique des nerfs traitee comme une polarization
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Mathematical aspects of Hodgkin-Huxley neural theory
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Savitzky-golay smoothing filters
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Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
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Fast sigmoidal networks via spiking neurons
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The vanishing gradient problem during learning recurrent neural nets and problem solutions
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Competitive hebbian learning through spike-timing-dependent synaptic plasticity
Sen Song, Kenneth D Miller, and Larry F Abbott · 2000
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Stable hebbian learning from spike timing-dependent plasticity
Mark CW Van Rossum, Guo Qiang Bi, and Gina G Turrigiano · 2000
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Fractional differentiation by neocortical pyramidal neurons
Brian N Lundstrom, Matthew H Higgs, William J Spain, and Adrienne L Fairhall · 2008
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Intrinsic stability of temporally shifted spike-timing dependent plasticity
Baktash Babadi and Larry F Abbott · 2010
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Engineering intelligent electronic systems based on computational neuroscience [scanning the issue]
Mark D McDonnell, Kwabena Boahen, Auke Ijspeert, and Terrence J Sejnowski · 2014
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Real-time segmentation of on-line handwritten arabic script
George Kour and Raid Saabne · 2014
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Neuronal dynamics: From single neurons to networks and models of cognition
Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Peter U Diehl and Matthew Cook · 2015
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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Spiking neural networks for handwritten digit recognition—supervised learning and network optimization
Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks
Khemraj Shukla, Patricio Clark Di Leoni, James Blackshire, Daniel Sparkman, and George Em Karniadakis · 2020
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Self-similar network model for fractional-order neuronal spiking: implications of dendritic spine functions
Jianqiao Guo, Yajun Yin, Xiaolin Hu, and Gexue Ren · 2020
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Backpropagation and the brain
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, and Geoffrey Hinton · 2020
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An ensemble of simple convolutional neural network models for mnist digit recognition
Sanghyeon An, Minjun Lee, Sanglee Park, Heerin Yang, and Jungmin So · 2020
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Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Youngeun Kim and Priyadarshini Panda · 2020
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Shruti R Kulkarni and Bipin Rajendran · 2018
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Neural-net-induced gaussian process regression for function approximation and pde solution
Guofei Pang, Liu Yang, and George Em Karniadakis · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G.E. Karniadakis · 2019
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fpinns: Fractional physics-informed neural networks
Guofei Pang, Lu Lu, and George Em Karniadakis · 2019
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Deep learning in spiking neural networks
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier, and Anthony Maida · 2019
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Going deeper in spiking neural networks: Vgg and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2019
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Brian 2, an intuitive and efficient neural simulator
Marcel Stimberg, Romain Brette, and Dan FM Goodman · 2019
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Physics-Informed Neural Networks for Heat Transfer Problems
Shengze Cai, Zhicheng Wang, Sifan Wang, Paris Perdikaris, and George Em Karniadakis · 2021
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Learning nonlinear operators via deeponet based on the universal approximation theorem of operators
Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, and George Em Karniadakis · 2021
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Non-invasive inference of thrombus material properties with physics-informed neural networks
Minglang Yin, Xiaoning Zheng, Jay D. Humphrey, and George Em Karniadakis · 2021
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Operator learning for predicting multiscale bubble growth dynamics
Chensen Lin, Zhen Li, Lu Lu, Shengze Cai, Martin Maxey, and George Em Karniadakis · 2021
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Training spiking neural networks using lessons from deep learning
Jason K Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D Lu · 2021
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Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport, 2022
Lu Lu, Raphael Pestourie, Steven G. Johnson, and Giuseppe Romano · 2022
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Physics-informed neural networks (pinns) for fluid mechanics: A review
Shengze Cai, Zhiping Mao, Zhicheng Wang, Minglang Yin, and George Em Karniadakis · 2022
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Analyses of internal structures and defects in materials using physics-informed neural networks
Enrui Zhang, Ming Dao, George Em Karniadakis, and Subra Suresh · 2022
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Nxtf: An api and compiler for deep spiking neural networks on intel loihi
Bodo Rueckauer, Connor Bybee, Ralf Goettsche, Yashwardhan Singh, Joyesh Mishra, and Andreas Wild · 2022
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Rate coding or direct coding: Which one is better for accurate, robust, and energy-efficient spiking neural networks?
Youngeun Kim, Hyoungseob Park, Abhishek Moitra, Abhiroop Bhattacharjee, Yeshwanth Venkatesha, and Priyadarshini Panda · 2022
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