NeuralOperator is now part of the #PyTorch Ecosystem, bringing a PyTorch-native library for learning neural operators and modeling mappings between function spaces for AI-driven science and engineering. 🔗 pytorch.org/blog/neuralopera… @JeanKossaifi @AnimaAnandkumar @davehpitt #AIInfrastructure #OpenSourceAI

Excited to share our recently published paper in @WileyGlobal on "Ocean Emulation With Fourier Neural Operators: Double Gyre" agupubs.onlinelibrary.wiley.… We used Fourier Neural Operators to build the first high-resolution weather model, FourCastNet. Since it works so well for atmospheric emulation a natural progression is to extend them to emulate ocean simulations. We propose learning the dynamics of a simplified ocean simulation using Fourier neural operators. Fourier neural operators. We are able to generate long forecasts using trained Fourier neural operators, and find that they are more accurate than using climatology or persistence on short-term forecasts and approach the accuracy of the physics-based model. On long-term forecasts, the neural operators can still predict future scenarios with realistic physics like propagating waves and meandering currents. This is impressive because no physics is explicitly programmed into the neural operators. Physics is learned from data. @Azizzadenesheli

We have released VARS-fUSI: Variable sampling for fast and efficient functional ultrasound imaging (fUSI) using neural operators. The first deep learning fUSI method to allow for different sampling durations and rates during training and inference. biorxiv.org/content/10.1101/… 1/

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 blocks for advanced used in your own workflow or to build new architectures.