Publications

Equal contribution is denoted by *

2025

  1. nn_to_no.png
    Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
    Julius Berner*, Miguel Liu-Schiaffini*, Jean Kossaifi, Valentin Duruisseaux, Boris Bonev, Kamyar Azizzadenesheli, and Anima Anandkumar
    arXiv preprint arXiv:2506.10973, 2025
    arXiv
  2. mri.png
    A Unified Model for Compressed Sensing MRI Across Undersampling Patterns
    Armeet Jatyani, Jiayun Wang, Aditi Chandrashekar, Zihui Wu, Miguel Liu-Schiaffini, Bahareh Tolooshams, and Anima Anandkumar
    Conference on Computer Vision & Pattern Recognition, 2025
    arXiv HTML
  3. no_cdft.png
    Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory
    Runtong Pan, Xinyi Fang, Kamyar Azizzadenesheli, Miguel Liu-Schiaffini, Mengyang Gu, and Jianzhong Wu
    arXiv preprint arXiv:2506.06623, 2025
    arXiv

2024

  1. local_no.png
    Neural Operators with Localized Integral and Differential Kernels
    Miguel Liu-Schiaffini*, Julius Berner*, Boris Bonev*, Thorsten Kurth, Kamyar Azizzadenesheli, and Anima Anandkumar
    41st International Conference on Machine Learning, 2024
    Spotlight at ICLR 2024 workshop arXiv Code
  2. nature.png
    Neural Operators for Accelerating Scientific Simulations and Design
    Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, and Anima Anandkumar
    Nature Reviews Physics, 2024
    HTML
  3. constraints.png
    Towards Enforcing Hard Physics Constraints in Operator Learning Frameworks
    Valentin Duruisseaux*, Miguel Liu-Schiaffini*, Julius Berner, and Anima Anandkumar
    ICML 2024 AI for Science Workshop, 2024
    PDF
  4. neuraloperator_logo.png
    A Library for Learning Neural Operators
    Jean Kossaifi, Nikola Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert Joseph George, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner, Valentin Duruisseaux, and  others
    arXiv preprint arXiv:2412.10354, 2024
    arXiv

2023

  1. tipping_point.png
    Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces
    Miguel Liu-Schiaffini, Clare E Singer, Nikola Kovachki, Tapio Schneider, Kamyar Azizzadenesheli, and Anima Anandkumar
    arXiv preprint arXiv:2308.08794, 2023
    arXiv

2022

  1. mno.png
    Learning Chaotic Dynamics in Dissipative Systems
    Zongyi Li*, Miguel Liu-Schiaffini*, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar
    Advances in Neural Information Processing Systems, 2022
    arXiv Code
  2. ice_picking.png
    Ice Thickness From Deep Learning and Conditional Random Fields: Application to Ice-Penetrating Radar Data With Radiometric Validation
    Miguel Liu-Schiaffini, Gregory Ng, Cyril Grima, and Duncan Young
    IEEE Transactions on Geoscience and Remote Sensing, 2022
    HTML