neural operators - Robuta Search

https://openreview.net/forum?id=7LSEkvEGCM&referrer=%5Bthe%20profile%20of%20Siddhartha%20Mishra%5D(%2Fprofile%3Fid%3D~Siddhartha_Mishra1) Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning |... Recently, operator learning, or learning mappings between infinite-dimensional function spaces, has garnered significant attention, notably in relation to... neural operators representation equivalent https://deepai.org/publication/learning-deep-implicit-fourier-neural-operators-ifnos-with-applications-to-heterogeneous-material-modeling Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material... Mar 15, 2022 - 03/15/22 - Constitutive modeling based on continuum mechanics theory has been a classical approach for modeling the mechanical responses of m... neural operators https://arxiv.org/abs/2209.02772?context=cs.LG [2209.02772] Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems Abstract page for arXiv paper 2209.02772: Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems neural operators 2209 02772 semi supervised https://jmlr.org/papers/v26/24-1985.html Neural Operators Can Play Dynamic Stackelberg Games neural operators play dynamic stackelberg games https://deepai.org/publication/u-no-u-shaped-neural-operators U-NO: U-shaped Neural Operators | DeepAI Apr 23, 2022 - 04/23/22 - Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g. function spaces. Prior wor... u no neural operators shaped deepai https://openreview.net/forum?id=knSgoNJcnV Uncertainty Quantification for Fourier Neural Operators | OpenReview In medium-term weather forecasting, deep learning techniques have emerged as a strong alternative to classical numerical solvers for partial differential... uncertainty quantification neural operators fourier openreview https://openreview.net/forum?id=MtekhXRP4h Convolutional Neural Operators for robust and accurate learning of PDEs | OpenReview Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function... neural operators robust https://openreview.net/forum?id=gpbBUE8uhp Variational Autoencoding Neural Operators | OpenReview Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and... variational autoencoding neural operators openreview https://openreview.net/forum?id=ToHkAg936Y Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws | OpenReview Neural operators (NOs) have emerged as effective tools for modeling complex physical systems in scientific machine learning. In NOs, a central characteristic... harnessing the power neural operators https://openreview.net/forum?id=rY6mQMW8nS EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D PDEs | OpenReview Deep learning surrogates for 3D Partial Differential Equations (PDEs) often fail to generalize across geometric transformations because they depend heavily on... neural operators equivariant geometry informed fourier https://openreview.net/forum?id=Q0C4jYZQ7x Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator... This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based... neural operators kernel knos https://openreview.net/forum?id=DPzQ5n3mNm Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric... neural operators https://openreview.net/forum?id=ZZTkLDRmkg BENO: Boundary-embedded Neural Operators for Elliptic PDEs | OpenReview Elliptic partial differential equations (PDEs) are a major class of time-independent PDEs that play a key role in many scientific and engineering domains such... neural operators beno boundary embedded elliptic https://openreview.net/forum?id=gangoPXSRw Probabilistic neural operators for functional uncertainty quantification | OpenReview Neural operators aim to approximate the solution operator of a system of differential equations purely from data. They have shown immense success in modeling... neural operators uncertainty quantification probabilistic functional openreview https://openreview.net/forum?id=ILmND4b1BK Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems | OpenReview Fourier Neural Operator (FNO) is a powerful and popular operator learning method. However, FNO is mainly used in forward prediction, yet a great many... neural operators https://openreview.net/forum?id=6Z0q0dzSJQ Neural Parameter Regression for Explicit Representations of PDE Solution Operators | OpenReview We introduce Neural Parameter Regression (NPR), a novel framework specifically developed for learning solution operators in Partial Differential Equations... neural parameter regression explicit https://arxiv.org/abs/2412.10354 [2412.10354] A Library for Learning Neural Operators Abstract page for arXiv paper 2412.10354: A Library for Learning Neural Operators a library for learning 2412 10354 neural https://openreview.net/forum?id=6aJrEC28hR Graph neural networks and non-commuting operators | OpenReview Graph neural networks (GNNs) provide state-of-the-art results in a wide variety of tasks which typically involve predicting features at the vertices of a... graph neural networks non commuting operators openreview https://arxiv.org/abs/2406.09795v2 [2406.09795v2] DeltaPhi: Physical States Residual Learning for Neural Operators in Data-Limited PDE... Abstract page for arXiv paper 2406.09795v2: DeltaPhi: Physical States Residual Learning for Neural Operators in Data-Limited PDE Solving https://arxiv.org/abs/2308.14789 [2308.14789] Scattering with Neural Operators Abstract page for arXiv paper 2308.14789: Scattering with Neural Operators 2308 scattering neural operators https://arxiv.org/abs/1912.03579 [1912.03579] Neural Networks with Cheap Differential Operators Abstract page for arXiv paper 1912.03579: Neural Networks with Cheap Differential Operators neural networks 1912 03579 cheap differential