miscellaneous-8

Neural Network simulation

3260 papers • 126 benchmarks • 313 datasets

Simulation of abstract or biophysical neural networks in silico

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Most implemented papers

BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python

H. Siegelmann, Hananel Hazan, D. J. Saunders, Hassaan Khan, Devdhar Patel, Darpan T. Sanghavi, R. Kozma•Sun Jun 03 2018

It is argued that this package facilitates the use of spiking networks for large-scale machine learning problems and some simple examples by using BindsNET in practice are shown.

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Neural Network-Hardware Co-design for Scalable RRAM-based BNN Accelerators

Yulhwa Kim, Hyungjun Kim, Jae-Joon Kim•Mon Nov 05 2018

Simulation results show that the proposed network reconstruction and retraining recovers the inference accuracy of the original BNN, and the accuracy loss of the proposed scheme in the CIFAR-10 testcase was less than 1.1% compared to the original network.

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Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

E. Haghighat, Daniel Amini, R. Juanes•Tue Oct 05 2021

This work presents a PINN approach to solving the equations of coupled flow and deformation in porous media for both single-phase and multiphase flow, and proposes a sequential training approach based on the stress-split algorithms of poromechanics.

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Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials

V. Tarokh, Jordan M. Malof, Yang Deng, Juncheng Dong, Simiao Ren, Omar Khatib, Mohammadreza Soltani, Willie J. Padilla•Thu Dec 31 2020

A collection of three classes of AEM problems: metamaterials, nanophotonics, and color designs is developed and methods and models that generalize best over the three problems are identified to establish the best practice and baseline results upon.

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Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

Chau Yuen, Chongwen Huang, Ronghong Mo•Sun Feb 23 2020

This paper develops a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action, and obtains the comparable performance compared with two state-of-the-art benchmarks.

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Application of machine learning regression models to inverse eigenvalue problems

Nikolaos Pallikarakis, Andreas Ntargaras•Wed Dec 07 2022

This work considers several examples of inverse problems and compares the performance of each model to predict the unknown potentials and refractive indices respectively, from a given small set of the lowest eigenvalues.

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