Wenzhe Sheng
Undergraduate Researcher
Peking University
School of Earth and Space Sciences
I work at the intersection of AI and geophysics. My current research focuses on deep learning methods for scientific problems, especially reinforcement learning, stochastic analysis, statistical field theory, and full waveform inversion for ChangE'4 Lunar Penetrating Radar data.
About Me
Here is Wenzhe Sheng (Volodymyr, 盛文哲). I am an undergraduate student in the School of Earth and Space Sciences at Peking University, China.
大家好,我是盛文哲,目前是北京大学地球与空间科学学院的本科生。 如果您对我的研究、学习笔记或主页内容感兴趣,欢迎通过邮件或社交媒体与我联系。
I value clarity in thinking and writing, and I use this website to keep track of research, learning notes, technical experiments, and personal reflections.
Research Interests
AI
Machine learning and deep learning methods for scientific discovery and inverse problems.
Geophysics
Seismic and radar wave propagation, subsurface imaging, and planetary science.
Reinforcement Learning
Sequential decision making and control in high-dimensional physical systems.
Stochastic Analysis
Stochastic processes, random fields, and uncertainty quantification in geosciences.
Statistical Field Theory
Field-theoretic methods for complex systems and scalable inference.
Full Waveform Inversion
Theory and algorithms for FWI with a focus on ChangE'4 Lunar Penetrating Radar data.
News and Updates
Redesigned this homepage for a cleaner academic reading experience.
Updated personal tracking materials and site assets.
Continued organizing blogs, diary records, and learning notes.
Started using this homepage to record research, learning, and personal life.
Explore More
Blogs
Thoughts on research, methods, learning, and science.
Browse all postsNotes
Study notes, course materials, technical summaries, and references.
Browse all notesDiary
A research and learning diary for reflections and records.
Browse diaryAbout this homepage
This website is built with Jekyll and GitHub Pages. I will keep updating notes, tutorials, blogs, and diary entries as a public record of my research and learning.