RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations | Paper Citation Record

Observation 6c6296a4-5ca4-41aa-af01-3b362cc1a1ed · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Highly accurate protein structure prediction with alphafold

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.536862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 123b45eb-8bd9-421f-b430-da38cfc80110 · outbound

This paper cites Prob- abilistic weather forecasting with machine learning.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Prob- abilistic weather forecasting with machine learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.516854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f9af946-bf81-4171-a1e2-4f96d6136c7c · outbound

This paper cites Denoising diffusion probabilistic models.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Denoising diffusion probabilistic models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T12:49:59.757206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8080c4a-ca3d-4dfe-9270-97a7de2ee367 · outbound

This paper cites Operator learning for predicting multiscale bubble growth dynamics.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Operator learning for predicting multiscale bubble growth dynamics

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.476464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a97fe3b-50fe-452b-b1ea-b8f1a3ca691d · outbound

This paper cites Systems biology informed deep learning for inferring parameters and hidden dynamics.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Systems biology informed deep learning for inferring parameters and hidden dynamics

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.454021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ce1b5f7c-2662-4de3-ae77-29d648c115e3 · outbound

This paper cites Promising directions of machine learning for partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Promising directions of machine learning for partial differential equations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.434745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e0a399c-4024-43ea-adf1-ba21bf1c7dd3 · outbound

This paper cites Artifi- cial intelligence for partial differential equations in computational mechanics: A review.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Artifi- cial intelligence for partial differential equations in computational mechanics: A review

Reference 7

Resolution: verified exact; raw_fallback, observed 2026-08-05T12:50:05.713545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea72d19b-6b6e-4ab0-a416-d82ebcb007ee · outbound

This paper cites Neural operator prediction of linear instability waves in high-speed boundary layers.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Neural operator prediction of linear instability waves in high-speed boundary layers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.410076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b4e0a869-fab1-4d7d-86da-deb8b4df0606 · outbound

This paper cites Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.383672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43227ff2-6733-4829-b810-a1dbe71577b8 · outbound

This paper cites A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.365619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 97fc8843-16d7-4550-972d-0cd853afc5f3 · outbound

This paper cites Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:50:05.347523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4add8b99-214b-492a-ad7e-58108887cb1f · outbound

This paper cites Data-driven iden- tification of parametric partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Data-driven iden- tification of parametric partial differential equations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.344690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 407ff9d9-0da7-4eb3-94c3-91531b601057 · outbound

This paper cites Data-driven deep learning of partial differential equations in modal space.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Data-driven deep learning of partial differential equations in modal space

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.323879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 17b88dd0-2fc6-464b-a60c-6e2630bd2306 · outbound

This paper cites Data driven approximation of parametrized PDEs by reduced basis and neural networks.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Data driven approximation of parametrized PDEs by reduced basis and neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.303640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 652bbe71-1b0a-47bb-b91b-9da1f6aea301 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:00.699728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b523307-53ce-42f3-b766-eea5ac7c4259 · outbound

This paper cites DeepXDE: A deep learning library for solving differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations DeepXDE: A deep learning library for solving differential equations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:00.778078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b8d8f13-39b2-462f-8b17-b1a0670fcaf7 · outbound

This paper cites Physics-informed machine learning.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Physics-informed machine learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.250474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5a621549-4d72-485c-8019-5541f9b4b362 · outbound

This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Scientific machine learning through physics–informed neural networks: Where we are and what’s next

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:00.932865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf11c3e3-bf91-421b-89e7-55cce2929e15 · outbound

This paper cites Physics-informed neural networks for inverse problems in nano-optics and metamaterials.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Physics-informed neural networks for inverse problems in nano-optics and metamaterials

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.209357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e90e9345-bff5-4ff2-a921-4008038607f9 · outbound

This paper cites PINNacle: A comprehensive benchmark of physics- informed neural networks for solving PDEs.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations PINNacle: A comprehensive benchmark of physics- informed neural networks for solving PDEs

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.189384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 14ba954e-1516-45e2-8bf5-9050deee8978 · outbound

This paper cites Automatic differentiation in PyTorch.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Automatic differentiation in PyTorch

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.167429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16663ee8-c1d5-436e-96e8-e8b17477b8f1 · outbound

This paper cites fPINNs: Fractional physics-informed neural networks.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations fPINNs: Fractional physics-informed neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.148221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 66b8df96-716a-415d-bcf4-73c9633c9fff · outbound

This paper cites Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.128362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26f65e6f-e05d-492c-bbff-c491ee5cbe9d · outbound

This paper cites Physics-informed neural networks with hard constraints for inverse design.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Physics-informed neural networks with hard constraints for inverse design

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.105254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1eb2ba94-68e3-4460-bbd9-62f728a06851 · outbound

This paper cites Dive into Deep Learning.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Dive into Deep Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:01.461851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0be45af0-dbc6-484a-a7cd-ecef55024625 · outbound

This paper cites Physics-informed multi-LSTM networks for meta- modeling of nonlinear structures.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Physics-informed multi-LSTM networks for meta- modeling of nonlinear structures

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.082361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0030afd3-7c3b-454a-8853-ac8d73055a22 · outbound

This paper cites Approximation theory of the MLP model in neural networks.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Approximation theory of the MLP model in neural networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:01.622817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c71d73ac-fb97-42d5-90f6-177d35093a72 · outbound

This paper cites Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:01.680448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71f914b4-f557-437e-863a-4f8d1f41dc1f · outbound

This paper cites Koop- man neural operator as a mesh-free solver of non-linear partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Koop- man neural operator as a mesh-free solver of non-linear partial differential equations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.034276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c251741e-6b82-4924-8b51-e3679f43e22c · outbound

This paper cites Approximations of continuous functionals by neural networks with application to dynamic systems.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Approximations of continuous functionals by neural networks with application to dynamic systems

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:07.015834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59a2bc01-9961-4f48-9e69-1aa69c0aa85c · outbound

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

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations A comprehensive and fair comparison of two neural operators (with practical extensions) based on F AIR data.Computer Methods in Applied Mechanics and Engineering, 393:114778, 2022

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.996577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8645447f-51ba-4608-ba9a-42b1712a3cf7 · outbound

This paper cites Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.976671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fa9a8e6-2bae-48ac-b427-16e3be83fe9d · outbound

This paper cites Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.958126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f2cc7c97-12e3-4028-9ee9-eb24982e9003 · outbound

This paper cites A scalable framework for learning the geometry-dependent solution operators of partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations A scalable framework for learning the geometry-dependent solution operators of partial differential equations

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.935282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba041a1b-54ef-4a52-a181-8d4c20e85b22 · outbound

This paper cites DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approxi- mation by neural networks.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approxi- mation by neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.910091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6a7fd179-30c0-4d77-9231-bfe4c37fd7e5 · outbound

This paper cites DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.880579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a26da21-cc06-4d37-8fad-dafaa3e6faed · outbound

This paper cites Stochastic operator network: A stochastic maximum principle based approach to operator learning.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Stochastic operator network: A stochastic maximum principle based approach to operator learning

Reference 37

Resolution: verified exact; raw_fallback, observed 2026-08-05T12:50:05.161148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73444aa6-3ea8-41cf-aeb5-bd8d599f40d7 · outbound

This paper cites Fundiff: Diffusion models over function spaces for physics-informed generative modeling.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Fundiff: Diffusion models over function spaces for physics-informed generative modeling

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:02.371896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6122911-2e2b-4eab-bac3-13bc195d199e · outbound

This paper cites Quantum DeepONet: Neural operators accelerated by quantum computing.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Quantum DeepONet: Neural operators accelerated by quantum computing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.855904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f159f134-6a07-41b9-b451-2d4402a39cfa · outbound

This paper cites MIONet: Learning multiple-input operators via tensor product.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations MIONet: Learning multiple-input operators via tensor product

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.834048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 579b8d1f-4920-4e90-9f3a-83a2f17c957e · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Fourier Neural Operator for Parametric Partial Differential Equations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:02.576753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c353b4f-7b82-42e5-bf71-22fc88bb0460 · outbound

This paper cites Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.808222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2fd71e35-759d-4543-8ddc-c2a67037beed · outbound

This paper cites Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.782369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ba11789-5ddb-445e-bf52-3dc032987f1d · outbound

This paper cites Laplace neural operator for solving differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Laplace neural operator for solving differential equations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.753605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5e9ec449-d0c7-4f3f-a063-c987daffb5da · outbound

This paper cites Efficient training of physics-informed neural networks via importance sampling.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Efficient training of physics-informed neural networks via importance sampling

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.731361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2057824-54c0-499a-a2a4-9232a68dcaa9 · outbound

This paper cites Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Gradient-enhanced physics- informed neural networks for forward and inverse pde problems

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.710135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76193420-4614-401e-b55d-8394267cbab3 · outbound

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

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.690861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1555c74a-fe4e-4914-a321-da5218faa833 · outbound

This paper cites Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.670234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0600118-5bee-4d9c-9b19-5179938cc9a0 · outbound

This paper cites Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:03.059365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9622b03-1486-427d-9ed6-8fc4fd35c006 · outbound

This paper cites Importance sampling: a review.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Importance sampling: a review

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.650040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5bedffef-dd01-4b76-b1ba-7018fe751b95 · outbound

This paper cites DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.627666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45927681-e8f3-4e04-b294-c2b35a5b4fa8 · outbound

This paper cites Deep adaptive sampling for surrogate modeling without labeled data.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Deep adaptive sampling for surrogate modeling without labeled data

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:50:04.687230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 611d599d-0ce7-4f99-b2dc-49fb33440c63 · outbound

This paper cites Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.608716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 654addb5-ef1e-4af7-9401-efc888914420 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Adam: A Method for Stochastic Optimization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:03.410931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d787b267-845c-43fc-9e55-6847e1705a8a · outbound

This paper cites PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular do- main.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular do- main

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.576110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 360b6ecc-cfba-4c55-8897-04bf0831751b · outbound

This paper cites Machine learning-based soil– structure interaction analysis of laterally loaded piles through physics-informed neural net- works.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Machine learning-based soil– structure interaction analysis of laterally loaded piles through physics-informed neural net- works

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.552130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e74d28e3-3d74-44ce-b16b-37791fbc88da · outbound

This paper cites Physics-informed neural net- works for large deflection analysis of slender piles incorporating non-differentiable soil-structure interaction.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Physics-informed neural net- works for large deflection analysis of slender piles incorporating non-differentiable soil-structure interaction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.529480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 110ff81e-6ebd-41fb-8767-e250c9762437 · outbound

This paper cites Kernel smoothing.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Kernel smoothing

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:03.679198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01023545-92e8-496d-b9c6-52f624a7d2f2 · outbound

This paper cites Neural topology optimization via active learning for efficient channel design in turbulent mass transfer.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Neural topology optimization via active learning for efficient channel design in turbulent mass transfer

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.492703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 22dedcc1-b717-4179-8f11-6f4384133a30 · outbound

This paper cites Active operator learning with predictive uncertainty quantification for partial differential equations.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Active operator learning with predictive uncertainty quantification for partial differential equations

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:03.831999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6860861f-d7ea-4932-bb9b-25f69ed85cb7 · outbound

This paper cites A collection of 2D elliptic problems for testing adaptive grid refinement algorithms.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations A collection of 2D elliptic problems for testing adaptive grid refinement algorithms

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.469455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 986c7854-b4c3-4898-b64a-634d77b63455 · outbound

This paper cites Learning the solution operator of para- metric partial differential equations with physics-informed DeepONets.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Learning the solution operator of para- metric partial differential equations with physics-informed DeepONets

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T12:50:04.006350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 967052f2-5860-445b-8072-cd98825c127f · outbound

This paper cites Global stabilization of two dimensional viscous Burg- ers’ equation by nonlinear Neumann boundary feedback control and its finite element analysis.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations Global stabilization of two dimensional viscous Burg- ers’ equation by nonlinear Neumann boundary feedback control and its finite element analysis

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:06.186343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e2283fa-3267-4b5c-a5af-08c8ed0eac86 · outbound

This paper cites PROSE: Predicting multiple operators and symbolic expressions using multimodal transformers.

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations PROSE: Predicting multiple operators and symbolic expressions using multimodal transformers

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:50:05.945445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers