Top Tweets for #neuraloperator
JCP 2026 | MD-PNOP: train neural operator once at single param -> extrapolate everywhere via equation recast (perturbation theory).
50% solver speedup, full-order accuracy. Architecture-agnostic (DeepONet + FNO verified).
https://t.co/sOm7W2XKEP
#NeuralOperator #PDE #ScientificML
#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
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- exceptionally less invasive,
2- data and energy efficient
3- and fast
taking us towards the future of real-time brain imaging.
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. https://t.co/hHoWJozejz 1/
A new #NeuralOperator for #Automotive industry, a leap towards new generation of modern engineering.
10x more accurate than prior art,
140,000x faster that conventional methods
Fully open source!
We present Factorized Implicit Global Convolution (FIGConvUNet) that is
GNO+3D_U-shapeFactorizedConv+GNO
Paper:https://t.co/jmFcJoHC4T
Code:https://t.co/kTgnHioDUn
According to experts, the accuracy almost matches the solver accuracy, which is important to conceive.
NSF article on our study in @NatComputSci - exciting!
https://t.co/VXeGvuIo93
@HopkinsEngineer @JHUBME @trayanovalab @minglang_y #cardiotwitter #AI #neuraloperator #DigitalTwin @JHU_ADVANCE @HopkinsDSAI @JohnsHopkins
#166 NeuralOperator: Simplifying Scientific Computing with PyTorch
#NeuralOperator #ScientificComputing #MachineLearning #AI #PyTorch #DataScience #Innovation #DataScienceDemystifiedDailyDose
https://t.co/RDauuBfenZ
NeuralOperator: A New Python Library for Learning Neural Operators in PyTorch
https://t.co/4WKUrqTBQJ
#NeuralOperator #OperatorLearning #ScientificComputing #AIResearch #MachineLearning #ai #news #llm #ml #research #ainews #innovation #artificialintelligence #machinelearning โฆ
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, to contribute and advance the library for better science.
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.
We've just published #continuiti 0.2.0! The new version features an improved documentation page (https://t.co/JRZHon5krK), some attention features, and a surprisingly effective #neuraloperator architecture we have termed #DeepCatOperator (DCO):
pip install -U continuiti
๐ข#AI4Science Talk on June-10th at 15:00 (CEST) / 09:00 EDT / 08:00 CDT on "HAMLET: Graph Transformer Neural Operator for Partial Differential Equations". If you're interested, please join on Zoom.
Details: https://t.co/Y3lnAjaGnE
#NeuralOperator #Transformers #GNNs #ML4Science
The way it works: Given data, you train a #NeuralOperator that maps GP to your data. You choose this operator to be invertible, and train it using maximum likelihood on stochastic processes. You use GP on one side, so computing point set value likelihood is straightforward.
We propose PhaseNO, a breakthrough #DeepLearning approach tackling one of the fundamental probs in #Seismology; seismic monitoring.
We develop a novel #NeuralOperator as #VirtualSeismologist allowing synced monitoring in a vast area of Earth, achieving precision/recall almst 1๐คฏ
See how virtual seismologists, using #generativeAI, revolutionize earthquake monitoring.
Our Phase Neural Operator, picks seismic phases simultaneously for any network geometry, leveraging spatio-temporal contextual info, by #NVIDIAResearch & @CalTech.
โก๏ธhttps://t.co/ATu3ExdEvv
In #DiffusionModels, often, a continuous function in time is computed to generate data. In this work, we develop a new #NeuralOperator architecture that allows us to directly predicts this function in one model evaluation, which is amazing. It achieves SOTA in both speed and FID.
Fast sampling of diffusion models. Only one model evaluation achieves SOTA! Check out poster at #NeurIPS22 SBM workshop on Friday 2:30pm-4pm at Room 293 - 294. @Kay12400259 @wn8_nie @ArashVahdat @Azizzadenesheli @nvidia @caltech
We introduce GeONet, a mesh-invariant deep neural operator for learning the Wasserstein geodesic connecting input pair of initial and terminal distributions.
https://t.co/8PT8fV9zGJ
#NeuralOperator #OptimalTransport
#GANO consists of two models, a generator #neuraloperator, and a functional discriminator. The inputs to the generator are samples of Gaussian random fields that are functions themselves. And the generator outputs function samples from the learned probability in infinite dim.
We now can use #NeuralOperator for #CarbonCapture & #Storage (#CCS) solutions, tens of thousands of times faster than before.
A new hope for tackling #globalwarming, & #climatechange
The model:
https://t.co/cXe7AlR7Qm
Please also check out the paper:
https://t.co/QotvcdP0qh
Excited to release our work on modeling #CarbonCapture #storage using #FNO that is tens of thousands of times faster than current #simulations @ZongyiLiCaltech @kazizzad @caltech @nvidia @stanford
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