Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

55
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
55

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Data Fusion of Deep Learned Molecular Embeddings for Property Prediction.

Journal of chemical information and modeling·2025
Same author

Predicting Hydrocarbon Strain Energy via a Group Equivalent Machine Learning Approach.

The journal of physical chemistry. A·2024
Same author

Effects of carbon concentration on the local atomic structure of amorphous GST.

The Journal of chemical physics·2024
Same author

Community action on FAIR data will fuel a revolution in materials research.

MRS bulletin·2024
Same author

High-throughput density functional theory screening of double transition metal MXene precursors.

Scientific data·2023
Same author

High-pressure and temperature neural network reactive force field for energetic materials.

The Journal of chemical physics·2023

相关实验视频

Updated: Jul 4, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K

使用节神经网络对能量材料的可解释性能模型.

Robert J Appleton1, Peter Salek2, Alex D Casey3

  • 1School of Materials Engineering and Birck Nanotechnology Center, Purdue University, West Lafayette, Indiana 47907, United States.

The journal of physical chemistry. A
|January 31, 2024
PubMed
概括

本研究介绍了使用宽松神经网络 (PNN) 的爆炸物和推进剂的可解释的预测模型. 这些模型平衡了材料选和设计的准确性和简单性.

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

585

相关实验视频

Last Updated: Jul 4, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

585

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 化学工程是化学工程的重要组成部分.

背景情况:

  • 预测模型对于爆炸物和推进剂的性能,设计和安全至关重要.
  • 目前的模型缺乏可解释性,阻碍了对材料物理和化学的洞察力.
  • 第一原则计算提供了基本数据,但在计算上是密集的.

研究的目的:

  • 开发可解释的模型来预测推进剂的特定冲动和爆炸性爆炸速度和压力.
  • 为了平衡模型准确性与简单性,以实现高效的材料选.
  • 为了深入了解能量材料的基础物理和化学.

主要方法:

  • 利用节神经网络 (PNN) 来发现可解释的模型.
  • 采用进化优化与自定义神经网络相结合.
  • 训练有素的模型使用客观函数,平衡准确性和复杂性.
  • 来自开放文献的数据来源.

主要成果:

  • 成功发现了特定冲动,爆炸速度和压力的可解释模型.
  • 确定了帕雷托最佳模型,为所有三个属性平衡准确性和简单性.
  • 证明PNN在创建可理解的预测模型方面的有效性.

结论:

  • 节神经网络为开发可解释的能量材料预测模型提供了一种可行的方法.
  • 开发的模型提供了预测能力和可解释性之间的平衡,有助于材料设计和发现.
  • 这项工作促进了更快的选和更深入地了解爆炸物和推进剂.