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https://www.tgs.com/technical-library/deep-learning-velocity-model-building-using-fourier-neural-operators- Deep Learning Velocity Model Building using Fourier Neural Operators | TGS We introduce a deep learning workflow that uses Fourier Neural Operators (FNOs) to estimate velocity models from field data with minimal pre-conditioning.... deep learning model building neural operators velocity using 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 geometry informed fourier https://slim.gatech.edu/content/amortized-velocity-continuation-fourier-neural-operators Amortized velocity continuation with Fourier neural operators | Seismic Laboratory for Imaging and... neural operators 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 https://slim.gatech.edu/content/fisher-informed-training-neural-operators-reliable-pde-inversion Fisher-Informed Training of Neural Operators for Reliable PDE Inversion | Seismic Laboratory for... neural operators https://experts.umn.edu/en/publications/beno-boundary-embedded-neural-operators-for-elliptic-pdes/ BENO: BOUNDARY-EMBEDDED NEURAL OPERATORS FOR ELLIPTIC PDES - Experts@Minnesota neural operators boundary embedded elliptic experts 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... the power of neural operators 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://www.sciencestack.ai/paper/2511.07347 Walsh-Hadamard Neural Operators for Solving PDEs with Discontinuous Coefficients... Nov 10, 2025 - Neural operators have emerged as powerful tools for learning solution operators of partial differential equations (PDEs). However, standard spectral methods bas neural operators walsh solving discontinuous coefficients https://www.tgs.com/technical-library/deep-learning-velocity-model-building-using-fourier-neural-operators- Deep Learning Velocity Model Building using Fourier Neural Operators | TGS We introduce a deep learning workflow that uses Fourier Neural Operators (FNOs) to estimate velocity models from field data with minimal pre-conditioning.... deep learning model building neural operators velocity using 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 geometry informed fourier https://researchconnect.buffalo.edu/en/publications/development-of-synthetic-ground-motion-records-through-generative/ Development of Synthetic Ground-Motion Records through Generative Adversarial Neural Operators -... development synthetic ground motion