Publications

Equal contribution is denoted by *

2024

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    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
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    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
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    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
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    Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators
    Armeet Jatyani, Jiayun Wang, Zihui Wu, Miguel Liu-Schiaffini, Bahareh Tolooshams, and Anima Anandkumar
    NeurIPS 2024 Workshop on Machine Learning and Compression, 2024
    arXiv

2023

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    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

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    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
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