Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators | Paper Citation Record

Observation f9970113-e57c-4fc7-a2e2-3ffbd5a9addc · outbound

This paper cites Finite volume methods.Handbook of numerical analysis, 7:713–1018, 2000.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Finite volume methods.Handbook of numerical analysis, 7:713–1018, 2000

Reference 1

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This paper cites Klaus-Jurgen Bathe, 2006.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Klaus-Jurgen Bathe, 2006

Reference 2

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This paper cites SIAM, 2007.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators SIAM, 2007

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This paper cites Smoothed particle hydrodynamics and its diverse applications.Annual Review of Fluid Mechanics, 44(1):323–346, 2012.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Smoothed particle hydrodynamics and its diverse applications.Annual Review of Fluid Mechanics, 44(1):323–346, 2012

Reference 4

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Observation 2e597795-e36c-4d0c-82a1-22c622597a00 · outbound

This paper cites A quasi-linear reproducing kernel particle method.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A quasi-linear reproducing kernel particle method

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Observation 31d6198a-f066-4b51-853d-a3e2222b1cb3 · outbound

This paper cites Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems.Jour- nal of Computational Physics, 397:108850, 2019.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems.Jour- nal of Computational Physics, 397:108850, 2019

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This paper cites fpinns: Fractional physics-informed neural networks.SIAM Journal on Scientific Computing, 41(4):A2603–A2626, 2019.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators fpinns: Fractional physics-informed neural networks.SIAM Journal on Scientific Computing, 41(4):A2603–A2626, 2019

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Observation 60fd96bd-58b9-478f-a219-b3e0b74c7f3f · outbound

This paper cites Deepxde: A deep learning library for solving differential equations.SIAM review, 63(1):208–228, 2021.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Deepxde: A deep learning library for solving differential equations.SIAM review, 63(1):208–228, 2021

Reference 8

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This paper cites Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.Computer Methods in Applied Mechanics and Engineering, 393:114823, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.Computer Methods in Applied Mechanics and Engineering, 393:114823, 2022

Reference 9

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Observation 23a1e5fb-b62a-47f1-b4ea-a0465e01c53d · outbound

This paper cites Physics-informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Physics-informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021

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This paper cites A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Reference 11

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Unresolved cited work

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Observation 27c40ae4-53e3-4cc6-a25d-1340c6ea97e1 · outbound

This paper cites An improved 2d finite element model for bolt load distribution analysis of composite multi-bolt single-lap joints.Composite Structures, 253:112770, 2020.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators An improved 2d finite element model for bolt load distribution analysis of composite multi-bolt single-lap joints.Composite Structures, 253:112770, 2020

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Observation c3c552a7-e5a7-4c58-9ce2-f7fddc385746 · outbound

This paper cites Modeling strategies of finite element simulation of reinforced concrete beams strengthened with frp: A review.Journal of Compos- ites Science, 5(1):19, 2021.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Modeling strategies of finite element simulation of reinforced concrete beams strengthened with frp: A review.Journal of Compos- ites Science, 5(1):19, 2021

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Observation ce79d44b-c41d-46d0-98c2-e36049203cd7 · outbound

This paper cites Finite element analysis of slope stability using a nonlinear failure criterion.Computers and Geotechnics, 34(3):127–136, 2007.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Finite element analysis of slope stability using a nonlinear failure criterion.Computers and Geotechnics, 34(3):127–136, 2007

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:22:01.857007Z

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Observation a20544cc-4d09-4924-97bf-b42253638589 · outbound

This paper cites On the convergence of overlapping elements and overlapping meshes.Computers & Structures, 244:106429, 2021.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators On the convergence of overlapping elements and overlapping meshes.Computers & Structures, 244:106429, 2021

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Observation d171b7b5-8020-468e-8847-b17b83db97ba · outbound

This paper cites Reduced-order modeling: new approaches for computational physics.Progress in aerospace sciences, 40(1-2):51–117, 2004.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Reduced-order modeling: new approaches for computational physics.Progress in aerospace sciences, 40(1-2):51–117, 2004

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Observation 3382f04b-bcbb-4a40-b4f4-1e4efc4dd099 · outbound

This paper cites Reduced-order methods for dynamic problems in topology optimization: A comparative study.Computer Methods in Applied Mechanics and Engineering, 387:114149, 2021.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Reduced-order methods for dynamic problems in topology optimization: A comparative study.Computer Methods in Applied Mechanics and Engineering, 387:114149, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:22:01.044879Z

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This paper cites John Wiley & Sons, 2009.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators John Wiley & Sons, 2009

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This paper cites A review: Applications of the spectral finite element method.Archives of Computational Methods in Engineering, 30(5):3453–3465, 2023.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A review: Applications of the spectral finite element method.Archives of Computational Methods in Engineering, 30(5):3453–3465, 2023

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Unresolved cited work

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Observation 82d31c71-8bfc-491e-8a0b-8b038c7d0653 · outbound

This paper cites Plastic hinge integration methods for force-based beam–column elements.Journal of Structural Engineering, 132(2):244–252, 2006.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Plastic hinge integration methods for force-based beam–column elements.Journal of Structural Engineering, 132(2):244–252, 2006

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Observation e5951791-7ef1-4f40-ab23-06c6b59003a7 · outbound

This paper cites A new mitc4+ shell element.Com- puters & Structures, 182:404–418, 2017.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A new mitc4+ shell element.Com- puters & Structures, 182:404–418, 2017

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This paper cites Galerkin formulations of isogeo- metric shell analysis: Alleviating locking with greville quadratures and higher-order elements.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Galerkin formulations of isogeo- metric shell analysis: Alleviating locking with greville quadratures and higher-order elements

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This paper cites Mionet: Learning multiple-input operators via tensor product.SIAM Journal on Scientific Computing, 44(6):A3490–A3514, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Mionet: Learning multiple-input operators via tensor product.SIAM Journal on Scientific Computing, 44(6):A3490–A3514, 2022

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Unresolved cited work

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This paper cites One-shot learning for solution operators of partial differential equations.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators One-shot learning for solution operators of partial differential equations

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This paper cites Coast: Intelligent time-adaptive neural operators.arXiv preprint arXiv:2502.08574, 2025.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Coast: Intelligent time-adaptive neural operators.arXiv preprint arXiv:2502.08574, 2025

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This paper cites Fundiff: Diffusion models over function spaces for physics-informed generative modeling.arXiv preprint arXiv:2506.07902, 2025.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Fundiff: Diffusion models over function spaces for physics-informed generative modeling.arXiv preprint arXiv:2506.07902, 2025

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This paper cites A physics-informed variational deeponet for predicting crack path in quasi-brittle materials.Computer Methods in Applied Mechanics and Engineering, 391:114587, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A physics-informed variational deeponet for predicting crack path in quasi-brittle materials.Computer Methods in Applied Mechanics and Engineering, 391:114587, 2022

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This paper cites Neural operators for accelerating scientific simulations and design.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Neural operators for accelerating scientific simulations and design

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Unresolved cited work

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This paper cites Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems.Nature Communications, 15(1):5101, 2024.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems.Nature Communications, 15(1):5101, 2024

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This paper cites On the training and generalization of deep operator net- works.SIAM Journal on Scientific Computing, 46(4):C273–C296, 2024.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators On the training and generalization of deep operator net- works.SIAM Journal on Scientific Computing, 46(4):C273–C296, 2024

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Unresolved cited work

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Efficient and generalizable nested fourier-deeponet for three-dimensional geological carbon sequestration

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Observation adcc74b8-f9f2-425b-b84f-40d04e8e5f8d · outbound

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A scalable framework for learning the geometry-dependent solution operators of partial differential equations.Nature Computational Science, 4(12):928–940, 2024

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Quantum deeponet: Neural operators accelerated by quantum computing.Quantum, 9:1761, 2025

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Unresolved cited work

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This paper cites A framework based on physics-informed neural networks and extreme learning for the analysis of composite structures.Computers & Structures, 265:106761, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A framework based on physics-informed neural networks and extreme learning for the analysis of composite structures.Computers & Structures, 265:106761, 2022

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Observation 6fd3ae8c-b816-4688-8c45-285150b3b78c · outbound

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Efficient neural topology optimization via active learning for enhancing turbulent mass transfer in fluid channels

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This paper cites Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Unresolved cited work

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Observation 6c75e8ec-8719-47f2-8384-e964dc3725e0 · outbound

This paper cites Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators

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Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators hp-vpinns: Variational physics-informed neural networks with domain decomposition.Computer Methods in Applied Mechanics and Engineering, 374:113547, 2021

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Observation d7ffa087-2030-4ace-985b-9e970a39cff3 · outbound

This paper cites Active Neuron Least Squares: A training method for multivariate rectified neural networks.SIAM Journal on Scientific Computing, 44(4):A2253– A2275, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Active Neuron Least Squares: A training method for multivariate rectified neural networks.SIAM Journal on Scientific Computing, 44(4):A2253– A2275, 2022

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:55.484755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ecf1528-b24b-4910-9af5-06d9c3dd33b5 · outbound

This paper cites Hierarchical deep learning neural network (hidenn): an artificial intelligence (ai) framework for computational science and engineering.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Hierarchical deep learning neural network (hidenn): an artificial intelligence (ai) framework for computational science and engineering

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:55.095617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c029f3c9-b198-4a35-b998-107cae63527b · outbound

This paper cites Exact dirichlet boundary physics- informed neural network epinn for solid mechanics.Computer Methods in Applied Mechanics and Engineering, 414:116184, 2023.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Exact dirichlet boundary physics- informed neural network epinn for solid mechanics.Computer Methods in Applied Mechanics and Engineering, 414:116184, 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:54.414875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 087cabc0-9b38-4588-b9b9-524ce6703b10 · outbound

This paper cites Finite operator learning: Bridging neural operators and numerical methods for efficient parametric solution and optimization of pdes.arXiv preprint arXiv:2407.04157, 2024.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Finite operator learning: Bridging neural operators and numerical methods for efficient parametric solution and optimization of pdes.arXiv preprint arXiv:2407.04157, 2024

Reference 52

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:21:49.944824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f58d98b2-0a89-4c50-ab20-0bbbe318a9a6 · outbound

This paper cites Weak adversarial networks for high-dimensional partial differential equations.Journal of Computational Physics, 411:109409, 2020.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Weak adversarial networks for high-dimensional partial differential equations.Journal of Computational Physics, 411:109409, 2020

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:46.235004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d8f441c-d8e7-499e-b92d-f81bceb93f1f · outbound

This paper cites Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems.Computer methods in applied mechanics and engineering, 402:115027, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems.Computer methods in applied mechanics and engineering, 402:115027, 2022

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:53.970508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e87fb8c-c09a-4dfe-bc7d-f1c7bbd9c9bc · outbound

This paper cites Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition.Computer Methods in Applied Mechanics and Engineering, 427:117041, 2024.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition.Computer Methods in Applied Mechanics and Engineering, 427:117041, 2024

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:53.655100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dcfff330-9d95-4d90-a1f0-b6f12ee628f0 · outbound

This paper cites Why it is difficult to solve helmholtz problems with classical iterative methods.Numerical analysis of multiscale problems, pages 325–363, 2011.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Why it is difficult to solve helmholtz problems with classical iterative methods.Numerical analysis of multiscale problems, pages 325–363, 2011

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:53.392173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c320107-b075-48e4-a683-56fbbd92fa3d · outbound

This paper cites Critical success factors for modular in- tegrated construction projects: A review.Building research & information, 48(7):763–784, 2020.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Critical success factors for modular in- tegrated construction projects: A review.Building research & information, 48(7):763–784, 2020

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:53.124312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 556af38e-20bf-43a9-8670-b43ea2d2de36 · outbound

This paper cites A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.Computer Methods in Applied Mechanics and Engineering, 393:114778, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.Computer Methods in Applied Mechanics and Engineering, 393:114778, 2022

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:47.036509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f947a5a-2e86-4a5f-a41a-8f3bc4bb2e40 · outbound

This paper cites Fully convolutional network enhanced deeponet-based surrogate of predicting the travel-time fields.IEEE Trans- actions on Geoscience and Remote Sensing, 2024.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Fully convolutional network enhanced deeponet-based surrogate of predicting the travel-time fields.IEEE Trans- actions on Geoscience and Remote Sensing, 2024

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:52.698357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa7be118-795e-4d65-beef-cde5968450e8 · outbound

This paper cites Improving physics-informed DeepONets with hard constraints.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Improving physics-informed DeepONets with hard constraints

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:47.595154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94afcc7e-4a06-46f9-aaec-c25e53de474d · outbound

This paper cites Bayesian deep operator learning for homogenized to fine-scale maps for multiscale pde.Multiscale Modeling & Simulation, 22(3):956–972, 2024.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Bayesian deep operator learning for homogenized to fine-scale maps for multiscale pde.Multiscale Modeling & Simulation, 22(3):956–972, 2024

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:52.424914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64b7b74e-41eb-434c-924c-e5a57b7b4e93 · outbound

This paper cites PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:21:49.265131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e41cf396-31f6-409e-8223-090667825d65 · outbound

This paper cites Gnot: A general neural operator transformer for operator learning.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Gnot: A general neural operator transformer for operator learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:48.144994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bb5ff47-8e4e-4438-a6db-e264f1d9654f · outbound

This paper cites Pfnn: A penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries.Journal of Computational Physics, 428:110085, 2021.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Pfnn: A penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries.Journal of Computational Physics, 428:110085, 2021

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:51.954975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1900dfb1-f35b-4ce3-9119-a66d3b8dfe71 · outbound

This paper cites Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks.Computer Methods in Applied Mechanics and Engineering, 389:114333, 2022.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks.Computer Methods in Applied Mechanics and Engineering, 389:114333, 2022

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:48.376521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f35969d-8a4e-4096-9e0f-7b5ca0dcf483 · outbound

This paper cites Systems biology informed deep learning for inferring parameters and hidden dynamics.PLoS computational biology, 16(11):e1007575, 2020.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Systems biology informed deep learning for inferring parameters and hidden dynamics.PLoS computational biology, 16(11):e1007575, 2020

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:48.532977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d700ae3f-75f1-454c-bb43-fd322aa5cc64 · outbound

This paper cites Physics-informed neural networks with hard constraints for inverse design.SIAM Journal on Scientific Computing, 43(6):B1105–B1132, 2021.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators Physics-informed neural networks with hard constraints for inverse design.SIAM Journal on Scientific Computing, 43(6):B1105–B1132, 2021

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:51.414944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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