Computational Science

数据驱动代理模型

Data-driven surrogate models

Computational models that approximate complex physical systems or experiments using machine learning algorithms to enable rapid prediction and optimization / 使用机器学习算法近似复杂物理系统或实验的计算模型，以实现快速预测和优化

Knowledge type: 工艺技术
First proposed or discovered
Proposer: Turkey Farm member
Creator: 农场主
Last updated: 2026-11-07 23:05:52

Sub-knowledge points

Gaussian process regression / 数学理论Response Surface MethodologyArtificial Neural Networks

Higher-order knowledge points

Finite Element Analysis / 数学理论 Digital Twin / 数学理论Computational Fluid Dynamics

Main parameters and performance indicators

Training data: Input-output pairs from experiments or simulationsModel complexity: Trade-off between accuracy and computational costObjective function: Multi-objective criteria for optimization

Open problems

Model interpretabilityGeneralization to unseen conditionsUncertainty quantification

Related literature

ranked by community stars

Rapid prediction and multi-objective optimization of mechanical response of the CEPC detector barrel yoke based on data-driven surrogate modelsOpen source

数据驱动代理模型

Sub-knowledge points

Higher-order knowledge points

Main parameters and performance indicators

Open problems

Related products and links