数值逼近理论及动态预测-修正理论框架的建模及实验数据

数值逼近理论及动态预测-修正理论框架的建模及实验数据主要面向数值逼近、动态预测-修正理论的研究与应用需求建设。其背景源于相关课题研发过程，需系统留存理论验证与实验测试的核心数据，为理论优化、技术落地及成果沉淀提供支撑，具有重要的科研参考与实践应用意义。
数据来源为课题研发中的测试案例数值计算和仿真数据，包含电路设计、仿真日志、良率验证结果等关键信息，于2025年11月采集自北京航空航天大学服务器。采集采用分方案实施：数值逼近框架计算数据通过课题提出的高维积分点搜索法、贝叶斯神经网络法测试数学解析样例，收集结果与误差数据整合而成；动态预测-修正理论数据通过配置测试程序参数、初始化后在电路案例上测试，收集仿真日志并经仿真工具验证得到良率结果；相关成果则直接整理课题产出的论文与软著材料。
其中“数值逼近框架计算数据”存储高维积分点设计法、蒙特卡洛法对应的概率分布、误差数据及良率计算数据等；“动态预测-修正理论实验数据”存储电路设计案例、仿真日志及良率验证结果；“相关成果”含相关论文、软著等成果材料，全面覆盖理论建模、实验测试及成果输出全流程数据。

The modeling and experimental data of the numerical approximation theory and dynamic prediction-correction theoretical framework are constructed to address the research and application demands of numerical approximation and dynamic prediction-correction theories. Derived from the research and development process of relevant projects, this dataset necessitates the systematic retention of core data for theoretical verification and experimental testing, providing support for theoretical optimization, technology implementation and achievement accumulation, and holds substantial scientific research reference and practical application significance.

The data is sourced from numerical calculations and simulation data of test cases during project R&D, including key information such as circuit design, simulation logs and yield verification results. It was collected from the servers of Beihang University in November 2025. The collection was implemented via separate schemes:
- The numerical approximation framework calculation data is compiled by testing mathematical analytical examples using the high-dimensional quadrature point search method and Bayesian neural network method proposed in the project, and integrating the collected result and error data;
- The dynamic prediction-correction theory data is obtained by configuring test program parameters, performing initialization, testing on circuit cases, collecting simulation logs and verifying yield results through simulation tools;
- The relevant achievements are directly organized from the papers and software copyright materials produced by the project.

Specifically, the "numerical approximation framework calculation data" stores the probability distribution, error data and yield calculation data corresponding to the high-dimensional quadrature point design method and Monte Carlo method, etc.; the "dynamic prediction-correction theory experimental data" stores circuit design cases, simulation logs and yield verification results; the "relevant achievements" include achievement materials such as relevant papers and software copyrights, comprehensively covering the full-process data of theoretical modeling, experimental testing and achievement output.