🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

We're releasing the public beta of #NeuralOperator, 1.0. A ground up #Python library containing neural operator architectures, datasets, examples, running codes, and algorithms for ML on functions. As a collective effort, we invite researchers, in particular in #AInScience,

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

I agree with Jensen that the next wave of AI is physical understanding, especially the ability to model and simulate physics is essential. Watch my TED talk go.ted.com/animaanandkumar #CES #Nvidia #NVIDIAcosmos

Thrilled to receive this honor by TIME Great to see neural operators and AI + science be recognized!

I finally met Jean Kossaifi and enjoyed his talk on Neural Operators datascience@uw

Excited to share our new preprint: COAST: Intelligent Time-Adaptive Neural Operators! 🌊 We introduce a novel neural operator that learns to dynamically and intelligently adjust time step sizes for modeling dynamical systems from data. 🚀 doi.org/10.48550/arXiv…

Thank you for the honor!

Human, mouse, monkey brain imaging, Ultrasound imaging is about the study of wave functions and their functional inversion, constituting a critical path to brain imaging. As a problem on function spaces, we introduce a novel #NeuralOperator technology for imaging, that is 1-

This work has been keeping me awake for quite sometime, The secret sauce behind Adam is its Signum. This is just mind consuming, great work Antonio Orvieto and Robert M. Gower 🇺🇦. It is also a great news for efficiency and MLSys communities.

We developed the foundations of diffusion model and posterior sampling on function spaces. We carefully extend Tweedie principle to function spaces which sits at the core of our methods. We provide techniques for functional regressions/reconstruction given partial sample

Stable-ChebNet Stable long range dependency is essential for physics, social network, and science, Technically, information needs to traverse effectively through out deep learning models without dissipating by due to lack of design. We propose a fundamentally

The best undergraduate and graduate research awards at  Caltech  commencement this year were presented to research on Neural Operators by my students Zongyi Li and Miguel Liu-Schiaffini. This recognition is a testament to their passion and dedication, and the impact