Miguel Liu-Schiaffini

Principled approaches for extending neural architectures to function spaces for operator learning

J. Berner, M. Liu-Schiaffini, J. Kossaifi, V. Duruisseaux, B. Bonev, K. Azizzadenesheli, A. Anandkumar

Nature Mach. Intell.

arXiv HTML

A Unified Model for Compressed Sensing MRI Across Undersampling Patterns

A. Jatyani, J. Wang, A. Chandrashekar, Z. Wu, M. Liu-Schiaffini, B. Tolooshams, A. Anandkumar

CVPR

arXiv Project

Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory

R. Pan, X. Fang, K. Azizzadenesheli, M. Liu-Schiaffini, M. Gu, J. Wu

J. Chem. Phys.

arXiv HTML

Neural Operators with Localized Integral and Differential Kernels

M. Liu-Schiaffini, J. Berner, B. Bonev, T. Kurth, K. Azizzadenesheli, A. Anandkumar

ICML Spotlight at ICLR 2024 workshop

arXiv Code

Neural Operators for Accelerating Scientific Simulations and Design

K. Azizzadenesheli, N. Kovachki, Z. Li, M. Liu-Schiaffini, J. Kossaifi, A. Anandkumar

Nature Rev. Physics

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Towards Enforcing Hard Physics Constraints in Operator Learning Frameworks

V. Duruisseaux, M. Liu-Schiaffini, J. Berner, A. Anandkumar

ICML Workshop

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A Library for Learning Neural Operators

J. Kossaifi, N. Kovachki, Z. Li, D. Pitt, M. Liu-Schiaffini, R. George, B. Bonev, K. Azizzadenesheli, J. Berner, V. Duruisseaux, A. Anandkumar

Preprint

arXiv

Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces

M. Liu-Schiaffini, C. Singer, N. Kovachki, S. Leung, H. Bae, K. Azizzadenesheli, A. Anandkumar

Preprint

arXiv

Learning Chaotic Dynamics in Dissipative Systems

Z. Li, M. Liu-Schiaffini, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, A. Anandkumar

NeurIPS

arXiv Code

Ice Thickness From Deep Learning and Conditional Random Fields: Application to Ice-Penetrating Radar Data With Radiometric Validation

M. Liu-Schiaffini, G. Ng, C. Grima, D. Young

IEEE TGRS

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