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

48
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...
48
Modeling in Therapy01:26

Modeling in Therapy

65
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
65
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
36
Cognitive Learning01:21

Cognitive Learning

237
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
237

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

Nesting and Scheduling Problems for Additive Manufacturing: A Taxonomy and Review.

Additive manufacturing·2026
Same author

Impedance Spectroscopy on Pristine and Degraded Quantum Dot Light-Emitting Diodes.

ACS applied materials & interfaces·2026
Same author

Re-Emergence and Characterization of a Highly Pathogenic Getah Virus on a Pig Farm in Guangdong Province, China.

Microorganisms·2026
Same author

Analysis of fungal community structure and co-occurrence networks across vegetation types in volcanic lava habitats.

Frontiers in fungal biology·2026
Same author

Natural Products Targeting the PI3K/Akt/mTOR-Mediated Autophagy Pathway in Cancer Therapy: Recent Advances and Clinical Perspectives.

Journal of natural products·2026
Same author

A review of 3D printed medical implant design.

3D printing in medicine·2026
Same journal

Deblurring structural edges in variable thickness topology optimization via density-gradient-informed projection.

Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization·2026
Same journal

Reinforcement learning-based control co-design of digital twin-enabled full-vehicle active suspension systems.

Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization·2026
Same journal

A novel multi-thickness topology optimization method for balancing structural performance and manufacturability.

Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization·2026
Same journal

Adjoint-based PDE-constrained optimization of viscoelastic floating membrane for maximum wave power absorption.

Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization·2026
Same journal

Differentiable modelling and optimization of multi-planar slicing for multi-axis additive manufacturing.

Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization·2026
Same journal

Geometrically nonlinear high-fidelity aerostructural optimization for highly flexible wings.

Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization·2025
查看所有相关文章

相关实验视频

Updated: Jun 21, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

765

积极学习适应替代模型改进在高维问题.

Yulin Guo1, Paromita Nath2, Sankaran Mahadevan1

  • 1Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37235 USA.

Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization
|July 15, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种有效的方法,用于构建具有高维输入和输出的替代模型. 它使用主动子空间和自适应学习来提高复杂模拟的模型准确性,例如增材制造残余应力.

关键词:
积极学习是指积极学习.增材制造 增材制造是一种增材制造.高维度的高维度的高维度.替代模型的替代模型

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

543
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K

相关实验视频

Last Updated: Jun 21, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

765
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

543
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K

科学领域:

  • 计算科学与工程 计算科学与工程
  • 机器学习 机器学习
  • 材料科学 材料科学 材料科学

背景情况:

  • 替代模型对于计算上昂贵的模拟至关重要.
  • 现有的自适应式学习方法在高维度输出方面扎.
  • 精确建模复杂的系统,如增材制造具有挑战性.

研究的目的:

  • 开发一种有效的方法来构建和改进用于高维输入和输出问题的替代模型.
  • 解决当前适应式学习技术在处理复杂,高维度输出方面的局限性.
  • 为了提高模拟的准确性和效率,用于诸如增材制造等应用.

主要方法:

  • 确定了高维输出的主要组件和特征.
  • 应用主动子空间技术,为每个特征找到低维的输入子空间.
  • 开发了一种新的低维适应性学习策略,为高维输出提供了探索-利用平衡.
  • 将新的训练样本映射回物理模型运行的原始空间.

主要成果:

  • 在增材制造部件的数值模拟上证明了该方法的有效性.
  • 成功建模了具有空间可变性的高维余应力场.
  • 研究了各种适应性学习参数对表现的影响.

结论:

  • 拟议的方法有效地构建和改进了高维问题的替代模型.
  • 适应性学习策略通过平衡探索和开发,有效地处理高维度输出.
  • 这种方法为模拟复杂的工程过程 (如增材制造) 提供了重大进步.