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相关概念视频

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Updated: Jul 25, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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一个基于物理的自动神经网络生成框架,用于新兴设备建模.

Guangxin Guo1, Hailong You1, Cong Li1

  • 1School of Microelectronics, Xidian University, Xi'an 710071, China.

Micromachines
|June 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种自动物理信息神经网络 (AutoPINN) 框架,以应对半导体建模方面的挑战. 自动PINN确保设备的精确,物理一致的神经网络模型,加速开发.

关键词:
自动化机器学习 (AutoML)电路模拟电路模拟器紧型的模型是紧型的模型.新兴设备建模新兴设备建模场效应晶体管 (FET) 是一个神经网络的神经网络的神经网络身体知情的身体知情.一个半导体设备的半导体设备.

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科学领域:

  • 半导体设备建模模的半导体设备
  • 在工程领域的人工智能.
  • 计算物理学的计算物理.

背景情况:

  • 传统的基于方程的半导体建模面临着精度和时间的限制.
  • 基于神经网络 (NN) 的模型提供了潜力,但受到非物理行为和复杂结构优化的影响.
  • 现有的NN模型缺乏流性和单调性,限制了实际应用.

研究的目的:

  • 提出一个自动物理信息神经网络 (AutoPINN) 生成框架.
  • 在基于NN的紧模型中解决非物理行为.
  • 为了自动确定半导体设备模拟的最佳NN结构.

主要方法:

  • 开发了一个结合物理信息神经网络 (PINNs) 和两步自动神经网络 (AutoNN) 的框架.
  • PINN包含物理信息,以确保模型的有效性.
  • 在没有专家干预的情况下,AutoNN自动优化NN架构.

主要成果:

  • 在Gate-all-around晶体管设备上,AutoPINN框架实现了不到0.05%的误差.
  • 在高阶衍生品和单调性中证明了保持平滑性.
  • 通过测试错误和损失景观分析验证了有希望的概括能力.

结论:

  • 自动PINN有效地解决非物理行为,并自动选择NN结构.
  • 该框架加速新兴半导体设备的开发和模拟.
  • 这种方法为传统建模方法提供了强大而高效的替代方案.