Top Tweets for #NeuralOperators
🧬🧠 Our Φ-Framework: extracting genomic design patterns via measure-valued inverse problems & Fourier Neural Operators. From topological ill-posedness to neural Bayes estimators. #MathBio #CompBio #NeuralOperators
7/7
The only real 'novelty' is the shift from task-specific models to more general ones, but that’s an evolution, not a revolution.
#PDE
#NumericalStability
#PINNs
#NeuralOperators
#AppliedMathematics
#QuantFinance
#MathematicalFinance
#DeepHedging
https://t.co/js5qCXL1yi
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
👥 Matthew Lowery, John Turnage, Zachary Morrow et al.
#AIResearch #MachineLearning #NeuralOperators
🔗 https://t.co/yLAwgrbFFu
DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting
Preprint: This study identifies two failure modes in neural operators for fluid dy…
https://t.co/xfEyIpDz2B #AI #FluidDynamics #NeuralOperators #MachineLearning #Preprint #Arxiv #ScienceNews
Neural operators revolutionize ML by learning mappings in infinite dimensional spaces. They solve PDEs efficiently and generalize across physics domains. DeepONet and Fourier neural operators enable rapid prediction in scientific modeling. #NeuralOperators #AI #MachineLearning
#NeuralOperators learn physics through data.
We study long term prediction capability of #NeuralOperator on a hard task of ocean emulation with variable forcing, making me think very seriously about coupled weather ocean model, #THEModel
Excited to share our recently published paper in @WileyGlobal on "Ocean Emulation With Fourier Neural Operators: Double Gyre" https://t.co/MWXSZtbFBY
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
Point predictions don’t capture the full creep-time relationship. We need a model that predicts the entire curve for any input params. Enter FNO: it’s like a neural net that vibes with continuous functions. #NeuralOperators #FunctionalData
Functional Operators for Memory Routing in Mixture-of-Experts (MoE)
https://t.co/PR3TQQGu8m
Continue Reading on Substack: https://t.co/SlpBPo9bKJ
#MixtureOfExperts #FunctionalRouting #Transformers #DeepLearning #NeuralOperators #AIArchitecture #satmis
#NeuralOperators for real world seismology
Full waveform inversion on a real seismic sites, San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area.
Steps:
1- Develop a domain-informed architecture
2- Train on generated function data resemble real phenomena
3- Do inversion on real data
For the first time, we can remove the "linear approximation" limitation of prior art that for decades ignored importnat details in subsurface Earth. Now we understand our Earth with much strong lenses.
For the first time we can run massive models with little memory footprint thanks to our domain specialized HelmholtzNO(HNO) architecture.
Paper: https://t.co/P2HTIVwVBa
#SanGabriel #Chino #SanBernardino
Joint work of #NeuralOperatorLab at @Caltech, University of Utah, and @nvidia @NVIDIAAI
Joint with Caifeng Zou, @zross_, Robert W. Clayton , Fan-Chi Lin
I was invited to talk at the Department of Physics (#TXST) Colloquium and will present on “#NeuralOperators for Physics-Informed #AI in Ultrawide Bandgap (#UWBG) Semiconductor Research.”
When: April 2nd, 2025
Where: RFM-3219
Time: 5 – 6 PM
Not long ago, we pioneered the first AI-driven weather forecasting model using #NeuralOperators. Now, it's incredible to see AI officially integrated into operational forecasts by leading agencies like @ECMWF. The rapid adoption of AI in meteorology is truly remarkable—huge credit to the brilliant researchers driving this progress!
https://t.co/23GmvUmVPe
Revolutionize AI with NeuralOperator: Your Guide to Neural Operators
https://t.co/h2Mo2BYSQz
#deeplearning #machinelearning #modelgeneralization #neuraloperators #Pythonlibrary #PyTorch #scientificcomputing
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: https://t.co/TXr5edCBgj #NeurIPS2024 #ML #MRI #NeuralOperators
At #NeurIPS2024,
Join the D3S3 workshop on Dec 15th.
We all will discuss #AInScience, solvers, #NeuralOperators, computational methods, PDEs, PINNs, and #MLonFunctions.
And generally the future of AI research and technology.
https://t.co/9chkTGSm5e
.@ZongyiLiCaltech has done extremely impressive work in AI+Science at @Caltech from inventing Neural Operators to showing performance in a wide range of domains.
#GenAI now on functions. #NeuralOperators
A new branch of MLonFunctions that will supercharge many advances in science, engineering, and safety.
Thanks to @GeorgiaTech community for having me to deliver a lecture on "#GenAI on functions", in partial to @amiraliagz for the invite and great discussion.
A few of the slides:
https://t.co/bx9Dq4UhVK
In this work, we do not focus on "discretization agnostics" property since many existing methods, excluding the #NeuralOperators ones, do not posses this fundamental property, making their relevance questionable.
To address that, we also present a way to advance neural network architectures to #NeuralOperators and boost performance, accuracy, and relevance.
❌GP-regression --> ✅Universal functional regression
We No Longer need Gaussian assumption for functional regression. We can learn the prior over functions.
We can train a #NeuralOperators to learn any Stochastic Process, give us likelihood of any collection of points, and do Bayesian regression on function spaces.
This is amazing news for science where filtering, data assimilation, and functional data completion is all you need.
Yaozhong, @af_gao @zross_ , @Caltech @nvidia @NVIDIAAI
Published at TMLR 2024
https://t.co/AbajEKer9u
Dear Seattle friends, I am visiting the University of Washington @UW this week to deliver the colloquium talk on
"ML on Function and #NeuralOperators" discussing the bright era of science and engineering with neural operators
PM me if you are down for a chat. @nvidia @NVIDIAAI
I'll be speaking about "Neural Operators: A New Era of Scientific Computing" at ODSC West 2024 @_odsc !
We will explore the evolution of AI from neural networks to #NeuralOperators and how this transformation is revolutionizing scientific and engineering computing. #MLonFunctions
From predicting the weather and the stock market to designing vehicles and molecules to developing new materials, scientific computing is fundamental to our lives.
We'll look at how this cutting-edge technology, developed in collaboration with @AnimaAnandkumar 's lab, can help us understand and model complex natural phenomena, paving the way for exciting discoveries and innovations.
#ODSCWest #AI #DataScience #MachineLearning @nvidia @NVIDIAAI @Caltech
We’re thrilled to announce that Kamyar Azizzadenesheli, Research Staff at Nvidia, will be speaking at #ODSCWest this October.
✅ Register now:
https://t.co/KAJqcWJa63
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