Peter Yichen Chen

Postdoctoral Researcher
Massachusetts Institute of Technology

Hello! This is Peter. Thanks for dropping by!

My research revolves around the intersection of artificial intelligence (AI) and physics simulation. Physics simulation has become the third pillar of science and engineering, alongside theory and experiments. Two distinct simulation paradigms have emerged: the classical laws of physics approach, e.g., leveraging partial differential equations (PDEs) derived from first principles, and the data-driven approach, e.g., training neural networks from observations. My research asks: how can we organically integrate these two approaches to amplify their respective strengths?

Along with my collaborators, We publish in top venues of machine learning, computer graphics, scientific computing, mechanics, robotics, and more. If there is any idea that you would like to bounce off, please do not hesitate to contact me!

Currently, I am a postdoc at MIT CSAIL, working with Wojciech Matusik. I completed my CS PhD from Columbia, advised by Eitan Grinspun. Before this, I was a math undergrad at UCLA (#GoBruins), mentored by Joey Teran.

tutorials

  1. Warp: Differentiable Spatial Computing for Python
    Miles Macklin, Nicolas Capens, Zeshun Zong, Pingchuan Ma, Peter Yichen Chen, Gergely Klar
    SIGGRAPH Courses 2024
    Project Webpage

selected publications

  1. How Can Large Language Models Help Humans in Design and Manufacturing?
    Liane Makatura, Michael Foshey, Bohan Wang, Felix HähnLein, Pingchuan Ma, Bolei Deng, Megan Tjandrasuwita, Andrew Spielberg, Crystal Elaine Owens, Peter Yichen Chen, Allan Zhao, Amy Zhu, Wil J Norton, Edward Gu, Joshua Jacob, Yifei Li, Adriana Schulz, Wojciech Matusik
    Harvard Data Science Review (HDSR) 2024
    Project Webpage
  2. LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields
    Chang Yue, Peter Yichen Chen+, Maurizio Chiaramonte, Kevin Carlberg, Eitan Grinspun+
    SIGGRAPH ASIA 2023
    Project Webpage PDF
  3. Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
    Zeshun Zong, Xuan Li, Minchen Li, Wojciech Matusik, Eitan Grinspun, Maurizio Chiaramonte, Kevin Carlberg, Chenfanfu Jiang, Peter Yichen Chen
    SIGGRAPH ASIA 2023
    Project Webpage PDF
  4. Implicit Neural Spatial Representations for Time-dependent PDEs
    Honglin Chen*, Rundi Wu*, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen
    International Conference on Machine Learning (ICML) 2023
    Project Webpage PDF
  5. Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
    Pingchuan Ma+, Peter Yichen Chen+, Bolei Deng, Joshua B. Tenenbaum, Tao Du, Chuang Gan, Wojciech Matusik
    International Conference on Machine Learning (ICML) 2023 (notable-top-25%)
    Project Webpage PDF
  6. CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
    Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G A Pershing, Henrique Teles Maia, Maurizio M Chiaramonte, Kevin Thomas Carlberg, Eitan Grinspun
    International Conference on Learning Representations (ICLR) 2023
    Project Webpage PDF
  7. Model reduction for the material point method via an implicit neural representation of the deformation map
    Peter Yichen Chen, Maurizio Chiaramonte, Eitan Grinspun, Kevin Carlberg
    Journal of Computational Physics (JCP) 2023
    PDF
  8. PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
    Xuan Li, Yi-Ling Qiao, Peter Yichen Chen, Krishna Murthy Jatavallabhula, Ming Lin, Chenfanfu Jiang, Chuang Gan
    International Conference on Learning Representations (ICLR) 2023 (notable-top-25%)
    Project Webpage
  9. Hybrid discrete-continuum modeling of shear localization in granular media
    Peter Yichen Chen, Maytee Chantharayukhonthorn, Yonghao Yue, Eitan Grinspun, Ken Kamrin
    Journal of the Mechanics and Physics of Solids (JMPS) 2021
    PDF
(*: Co-first authors, +: Corresponding authors)