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Drop by our #ICML2024 posters to chat about neural operators and PDE solvers 1⃣Solving Poisson Eqs. using Neural Walk-on-Spheres 2⃣DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training 3⃣Neural Operators w/ Localized Integral & Differential Kernels

📢 Come by our #ICML2024 poster "Neural Operators with Localized Integral and Differential Kernels"! 🚀 We propose two extensions of standard convolutional layers to operator learning! Location: Poster #216, 11:30 - 13:00 CEST Prof. Anima Anandkumar Julius Berner Kamyar Azizzadenesheli

#NeurIPS I am on the 2024-25 job market seeking faculty positions and postdocs! My goal is to advance AI for scientific computing and discovery. I develop neural operators for partial differential equations (PDEs) with applications in fluid, solid, and earth science.

Excited to present at NeurIPS 2024! 🎉 We propose a unified neural operator for Compressed Sensing MRI, adapting to multiple undersampling patterns/rates, with 11% SSIM & 4dB PSNR gains. Full paper & code: armeet.ca/nomri #NeurIPS2024 #ML #MRI #NeuralOperators

Introducing NeuralOperator 1.0: a Python library that aims at democratizing neural operators for scientific applications by providing all the tools for learning neural operators in PyTorch : state-of-the-art models, built-in trainers for quick starting and modular neural operator

This release was long in the making and the result of a large group effort. Check out our white paper: arxiv.org/abs/2412.10354 With Zongyi Li, Nikola Kovachki, David Pitt, Miguel Liu-Schiaffini, @Robertljg, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner and Prof. Anima Anandkumar

2024 was a pivotal year for AI+Science. Our team made exciting contributions. Here are some highlights: tensorlab.cms.caltech.edu/users/anima/20… 1. Neural Operators as a unifying AI framework for modeling multi-scale processes. We got to write a perspective article in Nature Reviews Physics and released an