方向：理工
专业：工程
是否可以加论文：否
项目时长及形式：英文
产出：
4周在线科研学习+3周论文辅导学习
加州大学伯克利分校工程学院官方结课证书
加州大学伯克利分校工程学院教授纸质推荐信
加州大学伯克利分校工程学院成绩单
EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等级别索引国际会议全文投递与发表（可用于申请）
项目介绍：
项目中，导师将首先带领学生回顾概率与统计基础，包括计数问题、样本空间、概率、事件以及连续和离散随机变量。在上述数学工具基础上，学生将进一步学习如何用模拟电路完成代数和微分方程系统建模、晶体管建模基础知识以及如何将晶体管用于构建数字电路。此外，学生还将学习建模和模拟仿真技术、数值方法及其在解决物理问题中的应用，譬如能源系统、材料工程、结构性分析、动力学和控制等工程师和科学家们在职业生涯中可能遇到的真实场景问题。项目后期，导师将讲解集群智能体的建模及如何使用机器学习优化来训练集群。学生还将掌握如何建模和模拟仿真更为复杂的系统，例如3D打印机器人的动力。以及用于系统优化的多物理场电动控制和机器学习。
个性化研究课题参考 Suggested Research fields
微机电系统智仿真
机器学习在石油管道泄露检测中的应用
数字超大规模集成电路设计
土木工程中可展结构的可靠性分析
Students will firstly review the foundations of probability and statistics, covering counting problems, sample spaces, probabilities, events, and continuous and discrete random variables. Then, Build on the mathematical tools taught earlier in the program, professor will explain how analog circuits can be modeled as systems of algebraic and differential equations, the basics of transistor modeling, and how transistors can be used to build digital circuits. Students will also learn techniques of modeling and simulation, numerical methods, and their implementation on physical problems motivated by real-world phenomena that engineers and scientists are likely to encounter in their careers involving energy systems, materials engineering, structural analysis, dynamics, controls, etc. At later stage in this program, under the guidance of the professor, students are expected to gain knowledge in the modeling of swarm “agents”, their dynamics and numerical methods to describe their motion. Learn how to train a swarm using machine-learning optimization. The final topic will be covered is to model and simulate more complex systems, such as the dynamics of a 3D printing robot, along with multiphysical electrodynamic control and machine-learning for system training/optimization.
Suggested Research fields
MEMS and Intelligent Physical Cyber Systems
Machine Learning in Pipeline Leak Detection
Digital VLSI Design
Reliability Analysis in Deploy-able Structures
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