您好，我是宋泽华👋，一名致力于运用人工智能方法解决复杂物理系统问题的研究者。
我硕士毕业于中国石油大学（北京）人工智能专业，师从杨进教授和宋宇副教授。我的研究聚焦于构建面向实际工程问题的预测建模与决策优化方法。
硕士期间，我在国内外期刊及会议上发表论文10余篇，其中6篇为第一作者或主要贡献者，并与中国海油、中国石油、金山云等企业开展了项目合作。
我获得了德州农工大学博士研究卓越奖学金，该奖项为新生入学最高荣誉，德州大学系统每年仅授予4人，并入选人民日报国家奖学金获奖学生代表名录，是全国100名入选者之一。
如果您对我的工作感兴趣，欢迎随时与我交流联系。
荣誉与奖项
查看我的最新作品
多孔结构潜空间可控生成与逆向设计工具
多孔结构潜空间可控生成与逆向设计工具
体素化正四面体多孔结构样本，训练潜空间模型解耦孔洞数量与孔径特征。给定目标孔洞数量与平均半径，经潜空间采样与连通域判定搜索候选结构，实现宏观到微观的逆向生成。以结构连通性与特征匹配度为目标，评估生成可控性与多样性。
钻头动力学参数辨识与累积做功解析工具
钻头动力学参数辨识与累积做功解析工具
基于录井数据与随钻高频振动测量，反演井底动力学参数以量化粘滑、跳钻及横向涡动引发的能量耗散，建立能量-磨损映射关系以评估钻头切削结构的退化状态与剩余寿命，并最终以最小化磨损与最大化钻进效率为目标，推荐井段最优钻井参数。
发表论文
审稿中
PeSNO: A Physics-embedded Stream Neural Operator as a Warm-Start Preconditioner for Mantle Convection Solvers
PeSNO: A Physics-embedded Stream Neural Operator as a Warm-Start Preconditioner for Mantle Convection Solvers
针对地幔对流模拟问题，提出流神经网络预测流函数以构造无散度速度场，作为数值求解器的暖启动预处理器，将线性求解器迭代次数减少一半以上。
作者： Zehua Song, Yu Song, Jin Yang
Hierarchical Bayesian Physics-Informed Network Model for Open-Hole Wellbore Instability: From Parameter Uncertainty to Reliability Assessment
Hierarchical Bayesian Physics-Informed Network Model for Open-Hole Wellbore Instability: From Parameter Uncertainty to Reliability Assessment
针对裸眼井壁失稳问题，构建嵌入分层贝叶斯推断的物理信息网络以量化参数不确定性，提供动态失效时间剖面。
作者： Zehua Song, Yu Song, Jin Yang, Kejin Chen, Jianqiao Zhang, Xing Wang
Intelligent Prediction and Optimization of Open-Hole Wellbore Multiphysics Stability: A Synergistic PINN-DRL Approach
Intelligent Prediction and Optimization of Open-Hole Wellbore Multiphysics Stability: A Synergistic PINN-DRL Approach
针对井筒多物理场稳定性问题，构建物理信息网络与深度强化学习协同框架，实现高速状态预测与钻井液参数优化。
作者： Yu Song, Zehua Song, Jin Yang, Kejin Chen, Kun Jiang, Jizhou Tang
已发表
Enhancing Energy Efficiency and Sustainability in Offshore Drilling through Real-Time Multi-Objective Optimization: Considering Lag Effects and Formation Variability
Enhancing Energy Efficiency and Sustainability in Offshore Drilling through Real-Time Multi-Objective Optimization: Considering Lag Effects and Formation Variability
针对海上钻井多目标优化问题，建立显式考虑地层变异与测量滞后效应的优化框架，经现场数据验证可提升钻进效率。
作者： Yu Song, Zehua Song, Jin Yang, Kejin Chen, Jizhou Tang
A Multi-Objective Reinforcement Learning Framework for Real-Time Drilling Optimization Based on Symbolic Regression and Drilling Perception
A Multi-Objective Reinforcement Learning Framework for Real-Time Drilling Optimization Based on Symbolic Regression and Drilling Perception
针对实时钻井参数优化，构建符号回归与深度强化学习相结合的优化方法，实现不同工况下的自适应优化。
作者： Zehua Song, Yu Song, Jin Yang, Baosheng Liu, Bingzhen Gao, Jizhou Tang
A Real-Time Inversion Framework for Carbon Equivalent Emissions in Oil and Gas Extraction Based on Vision Transformer
A Real-Time Inversion Framework for Carbon Equivalent Emissions in Oil and Gas Extraction Based on Vision Transformer
针对油气开采中的碳排放量化问题，构建Vision Transformer实时反演框架，实现碳排放当量的空间分布分析。
作者： Zehua Song, Xiaoyang Yu, Yu Song, Jin Yang, Dongsheng Xu, Kejin Chen, Fangfei Huang, Bin Chen, Yanwei Song
Multi-Objective Optimization Framework for Deepwater Riser Jetting Installation Parameters Using Deep Reinforcement Learning
Multi-Objective Optimization Framework for Deepwater Riser Jetting Installation Parameters Using Deep Reinforcement Learning
针对深水导管喷射安装参数优化，建立集成喷射时间预测模型与力学机理分析的深度强化学习优化框架。
作者： Yu Song, Zehua Song, Jin Yang, Lei Li
Early Warning of Deep-Water Drilling Influx Based on Machine Learning
Early Warning of Deep-Water Drilling Influx Based on Machine Learning
针对深水钻井溢流早期预警，构建机器学习预警方法，结合旁通管信号增强与延迟消除策略。
作者： Qishuai Yin, Zehua Song, Kejin Chen, Xu Zhou, Mayank Tyagi, Li Li
A Parzen-Estimator-Enhanced Bayesian Optimization Framework for Deepwater Conductor Jetting
A Parzen-Estimator-Enhanced Bayesian Optimization Framework for Deepwater Conductor Jetting
针对深水导管喷射工艺优化，提出分段贝叶斯优化框架，结合全局搜索与局部精化策略。
作者： Yu Song, Jin Yang, Zehua Song
合著
Deep Learning Based Early Warning Methodology for Gas Kick of Deepwater Drilling Using Pilot-Scale Rig Data
Deep Learning Based Early Warning Methodology for Gas Kick of Deepwater Drilling Using Pilot-Scale Rig Data
作者： Qishuai Yin, Qikang Zhu, Zehua Song, et al.
Research on Pore Pressure Prediction Technology of HTHP Wells in South China Sea Based on Machine Learning
Research on Pore Pressure Prediction Technology of HTHP Wells in South China Sea Based on Machine Learning
作者： Dongsheng Xu, Jin Yang, ..., Zehua Song, et al.
Digital Twin: Hybrid Virtual Simulation and Physical Monitoring Strategy for Subsea Wellhead Fatigue Assessment
Digital Twin: Hybrid Virtual Simulation and Physical Monitoring Strategy for Subsea Wellhead Fatigue Assessment
作者： Kejin Chen, Jin Yang, ..., Zehua Song, et al.
准备中
Adaptive Drilling Decision-Making Framework for Extended-Reach Wells under Multiple Objectives
Adaptive Drilling Decision-Making Framework for Extended-Reach Wells under Multiple Objectives
作者： Yu Song, Zehua Song, ……