Search Results for author: Miguel Liu-Schiaffini

Found 6 papers, 3 papers with code

A Unified Model for Compressed Sensing MRI Across Undersampling Patterns

no code implementations5 Oct 2024 Armeet Singh Jatyani, Jiayun Wang, Aditi Chandrashekar, Zihui Wu, Miguel Liu-Schiaffini, Bahareh Tolooshams, Anima Anandkumar

Our unified model offers a versatile solution for MRI, adapting seamlessly to various measurement undersampling and imaging resolutions, making it highly effective for flexible and reliable clinical imaging.

Anatomy compressed sensing +4

Neural Operators with Localized Integral and Differential Kernels

2 code implementations26 Feb 2024 Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar

In this work, we present a principled approach to operator learning that can capture local features under two frameworks by learning differential operators and integral operators with locally supported kernels.

Operator learning

Neural Operators for Accelerating Scientific Simulations and Design

no code implementations27 Sep 2023 Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar

Scientific discovery and engineering design are currently limited by the time and cost of physical experiments, selected mostly through trial-and-error and intuition that require deep domain expertise.

scientific discovery Super-Resolution +1

Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces

no code implementations17 Aug 2023 Miguel Liu-Schiaffini, Clare E. Singer, Nikola Kovachki, Tapio Schneider, Kamyar Azizzadenesheli, Anima Anandkumar

Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems.

Conformal Prediction

Learning Dissipative Dynamics in Chaotic Systems

2 code implementations13 Jun 2021 Zongyi Li, Miguel Liu-Schiaffini, Nikola Kovachki, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

Chaotic systems are notoriously challenging to predict because of their sensitivity to perturbations and errors due to time stepping.

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