Yilin Wang

Yilin Wang

Senior Undergraduate
@ Peking University

Visiting Undergraduate Researcher
@ Harvard University

Research

  • Radiative Effect of Dust on the Climate of Early Mars

    June 2025 - present

    Advisor: Robin Wordsworth

    Project, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, US

    • Calculated key radiative parameters for early Mars dust analogs by applying Mie scattering theory, generating look-up tables for radiative transfer models
    • Integrated custom Mie scattering outputs into the PCM_LBL 1D radiative-convective model to simulate the climatic impact of dust on early Mars
    • Using the theory of reflectance spectroscopy to determine the reflectance spectra of regolith

  • Cloud-Resolving Simulation of Precipitation in Planetary Atmospheres

    March 2025 - present

    Advisor: Jun Yang

    Project, School of Physics, Peking University, Beijing, China

    • Utilized the System for Atmospheric Modeling (SAM) to conduct cloud-resolving simulations of diverse planetary atmospheres
    • Analyzed the influence of varying solar spectra and surface gravity on precipitation patterns and atmospheric dynamics

  • Stability of Polar Vortices in Planetary Atmospheres

    January 2024 - September 2025

    Advisor: Tao Cai, Jun Yang

    Project, School of Physics, Peking University, Beijing, China

    • Analyzed the stability of planetary polar vortices by conducting 3D simulations and linear stability analysis to identify critical conditions leading to vortex splitting
    • Exploring the relationship between vortex instability and atmospheric parameters, with plans to extend the analysis to include various boundary conditions and flow fields

  • Planetary Atmospheric Spectrum Retrieval based on Machine Learning

    November 2024 - December 2024

    Course Project, School of Physics, Peking University, Beijing, China

    • Course project for Numerical Simulation and AI Forecast of Geophysics Fluids
    • Engineered a comprehensive synthetic dataset of planetary emission spectra for model training and validation using the pyratbay radiative transfer model
    • Designed, trained, and benchmarked six distinct machine learning architectures (including Linear Regression, MLP,CNNs, and Transformer) to comparatively analyze their performance in retrieving atmospheric composition and profiles