Personal Profile
Chen Yunpeng, Assistant Professor at the Hetao Institute of Mathematics and Interdisciplinary Sciences (Shenzhen). Awarded a PhD in Geophysics from the University of Leeds in 2025, during which he undertook joint training with the Southern University of Science and Technology. His research focuses on the interdisciplinary field of artificial intelligence and geophysics, dedicated to developing and applying AI methodologies to address complex geophysical forward and inverse problems constrained by partial differential equations. He has published papers as first author/corresponding author in Geophysical Research Letters and Journal of Geophysical Research: Solid Earth, with research honoured as a GRL Top Downloaded Article (Top 10%). He serves as a reviewer for journals including Journal of Geophysical Research: Solid Earth, Geophysics, and Science China: Earth Sciences.
Research Interests
Artificial Intelligence Geophysics
Physical Information Neural Networks
Seismic Wave Tomography and Inversion Theory
Research into the Deep Structure of the Earth
Educational Background
PhD/2020–2025 University of Leeds Geophysics
Master's Degree / 2018–2020 Harbin Institute of Technology Mechanics
Bachelor's Degree / 2014–2018 China University of Geosciences (Wuhan) Exploration Technology and Engineering
Publications
1）Chen Y, de Ridder S A L, Rost S, et al. Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau[J]. Geophysical Research Letters, 2022, 49(21): e2022GL099053.
2）Chen Y, de Ridder S A L, Rost S, et al. Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau[J]. Journal of Geophysical Research: Solid Earth, 2023, 128(12): e2023JB027378.
Yunpeng Chen
Research Area: Geophysics
1. Biography
Yunpeng Chen is an Assistant Professor at the Hetao Institute of Mathematics and Interdisciplinary Sciences (HIMIS), Shenzhen. He received his Ph.D. in Geophysics from the University of Leeds in 2025, jointly trained with the Southern University of Science and Technology. His research primarily focuses on the interdisciplinary field of AI + Geophysics. He is dedicated to developing and applying artificial intelligence methods to solve complex geophysical forward and inverse problems constrained by partial differential equations (PDEs). As a first/corresponding author, he has published papers in journals such as Geophysical Research Letters and the Journal of Geophysical Research: Solid Earth. His research has been honored as a GRL Top Downloaded Article (Top 10%). He serves as a reviewer for journals including the Journal of Geophysical Research: Solid Earth, Geophysics, and SCIENCE CHINA Earth Sciences.
2. Research Interests
· AI Geophysics
· Physics-Informed Neural Networks
· Seismic Tomography and Inversion Theory
· Deep Earth Structure
3. Education
· Ph.D. / 2020-2025 University of Leeds / Geophysics
· M.S. / 2018-2020 Harbin Institute of Technology / Mechanics
· B.S. / 2014-2018 China University of Geosciences (Wuhan) / Exploration Technology and Engineering
4. Publications
1. Chen Y, de Ridder S A L, Rost S, et al. Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau[J]. Geophysical Research Letters, 2022, 49(21): e2022GL099053.
2. Chen Y, de Ridder S A L, Rost S, et al. Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau[J]. Journal of Geophysical Research: Solid Earth, 2023, 128(12): e2023JB027378.