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Updated: Jun 10, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

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提高与特征的融合速度,强制执行物理信息的神经网络,使用边界条件作为先验知识

Mahyar Jahani-Nasab1, Mohamad Ali Bijarchi2

  • 1Center of Excellence in Energy Conservation (CEEC), Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.

Scientific reports
|October 11, 2024
PubMed
概括

本研究提出了一种更快的训练方法,通过优化初始权重和点分布来为物理信息神经网络 (PINNs) 提供更快的训练方法. 这种方法改善了趋同,并消除了手动超参数调整的需要.

关键词:
损失加权是对损失进行加权的.基于物理学的神经网络.科学机器学习科学机器学习

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