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相关概念视频

Colloids and Suspensions01:17

Colloids and Suspensions

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Children at play often make suspensions such as mixtures of mud and water, flour and water, or a suspension of solid pigments in water known as tempera paint. These suspensions are heterogeneous mixtures composed of relatively large particles visible to the naked eye or seen with a magnifying glass. They are cloudy, and the suspended particles settle out after mixing. The suspended particles in a suspension settle out after some time of mixing. The separation of particles from a suspension is...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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通过机器学习导航小型数据集:用于体凝的高斯过程建模.

Rohan Batra1, Yogesh M Joshi1, Sachin Shanbhag2

  • 1Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India.

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概括
此摘要是机器生成的。

本研究介绍了高斯过程 (GP) 框架,用于用小型数据集进行机器学习,这对材料科学至关重要. 该方法有效地模拟合成参数和材料特性,使得发现新的材料路径成为可能.

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科学领域:

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

背景情况:

  • 机器学习方法往往需要大量的数据集,这给数据有限的科学学科带来了挑战.
  • 高斯过程 (GPs) 是强大的概率模型,擅长处理小数据集并提供不确定性量化.

研究的目的:

  • 开发一个数据驱动的框架,使用全科医生在具有有限实验数据的场景中进行建模和推理.
  • 探索单输出和多输出GPs的应用,用于将合成参数映射到合溶液-凝过渡特征.
  • 为反向问题建立贝叶斯框架,使得对所需材料属性的合成途径的发现成为可能.

主要方法:

  • 使用单输出和多输出结构的高斯过程 (GPs) 来建模合成参数和凝特性之间的关系.
  • 实施了贝叶斯框架,将GP模型与马尔科夫链蒙特卡洛 (MCMC) 采样集成,以解决反向问题.
  • 作为一个案例研究,将该方法应用于自发的合体sol-gel过渡.

主要成果:

  • 单输出和多输出GP结构都成功地量化了输入-输出关系.
  • 多个输出GPs通过同时学习不同输出变量之间的相关性来证明优异的不确定性校准.
  • 贝叶斯的GP框架成功地确定了实现特定材料性能的多个合成途径.

结论:

  • 拟议的基于GP的框架为材料科学中的数据受约束建模提供了一个强大的方法.
  • 这种方法对于合体领域的材料设计和其他面临数据限制的领域尤其有价值.
  • 该框架通过有效处理不确定性和反向问题,促进发现新型合成路径.