Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T11:09:38.087400Z
Paper Citation Record · LEDGER
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars
As of 17 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2507.23119.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
45 of 45 outbound references displayed
- verified exact2
- verified fuzzy37
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 77f06c5c-585b-4744-b654-510b96793bcc · outbound
This paper cites Optical metasurfaces: new generation building blocks for multi-functional optics,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Optical metasurfaces: new generation building blocks for multi-functional optics,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.852964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation dae791fd-0e5d-4617-9d53-f5c574ed1e5a · outbound
This paper cites Roadmap for optical metasurfaces,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Roadmap for optical metasurfaces,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.843150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ede437ab-b6b2-4272-a990-12a1b871537f · outbound
This paper cites Flat optics: controlling wavefronts with optical antenna metasurfaces,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Flat optics: controlling wavefronts with optical antenna metasurfaces,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.832492Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1d690348-0900-4c3d-a741-8f9d4d256908 · outbound
This paper cites Metasurface micro/nano-optical sensors: principles and applications,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Metasurface micro/nano-optical sensors: principles and applications,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.822796Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 095d87d9-d3ae-4786-9c20-bf0635e277aa · outbound
This paper cites Optical Metasurfaces for Biomedical Imaging and Sensing,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Optical Metasurfaces for Biomedical Imaging and Sensing,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.812705Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2927f14f-f60d-4304-bc79-38261b626eb6 · outbound
This paper cites 3D-Integrated metasurfaces for full-colour holography,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars 3D-Integrated metasurfaces for full-colour holography,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.802760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 47bfab6c-c27e-4f67-83f1-89e4a89f4b1c · outbound
This paper cites Metasurfaces for multiplexed communi- cation,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Metasurfaces for multiplexed communi- cation,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.792608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 26d31f71-9995-4c04-ae09-27035cb65fb9 · outbound
This paper cites A programmable diffractive deep neural network based on a digital-coding metasurface array,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars A programmable diffractive deep neural network based on a digital-coding metasurface array,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.781411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f4eddf43-bcd2-49da-8606-44fd116e910b · outbound
This paper cites Time-Domain Finite-Difference and Finite-Element Methods for Maxwell Equations in Complex Media,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Time-Domain Finite-Difference and Finite-Element Methods for Maxwell Equations in Complex Media,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.770578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 213a48d6-d579-4fab-8fd1-37a1445c8585 · outbound
This paper cites Fast near field simulation of optical and EUV masks using the waveguide method,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Fast near field simulation of optical and EUV masks using the waveguide method,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.760873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0918b15e-204f-4699-a311-1163ea127632 · outbound
This paper cites Large-scale photonic inverse design: computational challenges and breakthroughs,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Large-scale photonic inverse design: computational challenges and breakthroughs,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.751341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5b0fa309-0aae-46bb-9340-c11ef9686c9f · outbound
This paper cites Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.741828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d42a8248-cc2a-4700-962c-d57a1a036088 · outbound
This paper cites PISC-Net: A Comprehensive Neural Network Framework for Predicting Metasurface Infrared Emission Spec- tra,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars PISC-Net: A Comprehensive Neural Network Framework for Predicting Metasurface Infrared Emission Spec- tra,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.732144Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 69cdc5b0-f90c-4b57-9510-3b5566c16471 · outbound
This paper cites Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.722199Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation cf966ab6-03df-4ad5-be5b-8c65b64b6e31 · outbound
This paper cites Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:09:35.033479Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b6e1723c-254e-4891-ac25-92bdccfeac9b · outbound
This paper cites From PINNs to PIKANs: recent advances in physics-informed machine learning,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars From PINNs to PIKANs: recent advances in physics-informed machine learning,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.712266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 062c9674-5c65-4989-b796-80b0c84ae634 · outbound
This paper cites Physics-Informed Neural Network Inte- grating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics-Informed Neural Network Inte- grating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.702520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f306eb97-2689-4978-b35e-efff3ac6a768 · outbound
This paper cites Physics- Informed Machine Learning for Industrial Reliability and Safety Engi- neering: A Review and Perspective,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics- Informed Machine Learning for Industrial Reliability and Safety Engi- neering: A Review and Perspective,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.692303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 878299ff-c731-48d6-a5b0-0cc10c63d5dc · outbound
This paper cites Loss landscape en- gineering via Data Regulation on PINNs,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Loss landscape en- gineering via Data Regulation on PINNs,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.682488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 64545e90-4967-4b60-8338-c0c1fd5f5cf6 · outbound
This paper cites An Expert's Guide to Training Physics-informed Neural Networks.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars An Expert's Guide to Training Physics-informed Neural Networks
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:09:35.294017Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ae39935-aed2-4895-9937-ca8b345150c2 · outbound
This paper cites Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.671055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1fd34257-f189-4afc-b78b-1a5f23c00329 · outbound
This paper cites Maxwellnet: Physics-driven deep neural network training based on maxwell’s equations,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Maxwellnet: Physics-driven deep neural network training based on maxwell’s equations,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.660863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ebee71de-54a7-43ee-8c54-154bcf188434 · outbound
This paper cites Physics- informed deep model for fast time-domain electromagnetic simula- tion and inversion,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics- informed deep model for fast time-domain electromagnetic simula- tion and inversion,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.640824Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f0dfd6ed-eae6-4880-b830-39d9074c3c9b · outbound
This paper cites Multi-receptive-field physics-informed neural network for complex electromagnetic media,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Multi-receptive-field physics-informed neural network for complex electromagnetic media,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.543337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation cc7f6ca3-fa21-4006-88ec-5aaaf53dff4f · outbound
This paper cites Inverse Design for Integrated Photonics Using Deep Neural Network,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Inverse Design for Integrated Photonics Using Deep Neural Network,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.478326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 974ec7dd-500e-4cd0-a222-92e86319fbb2 · outbound
This paper cites 3D EUV mask simulator based on physics-informed neural networks: effects of polarization and illumination,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars 3D EUV mask simulator based on physics-informed neural networks: effects of polarization and illumination,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.203223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bfb3a443-1cd3-4a92-b490-71d7024c73dd · outbound
This paper cites 3D mask simulation and lithographic imaging using physics-informed neural networks,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars 3D mask simulation and lithographic imaging using physics-informed neural networks,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:42.108035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 53ab3e9f-66b8-4169-8e5b-d91ebbce6f30 · outbound
This paper cites Physics-informed deep learning for 3D modeling of light diffraction from optical metasurfaces,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics-informed deep learning for 3D modeling of light diffraction from optical metasurfaces,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:41.831058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation cd71ff52-debd-4707-9a2c-8c34519a16fc · outbound
This paper cites The Helmholtz equation in heterogeneous media: A priori bounds, well-posedness, and resonances,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars The Helmholtz equation in heterogeneous media: A priori bounds, well-posedness, and resonances,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:41.507216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d1b7830b-0efd-4a3a-b717-8123946f7ce6 · outbound
This paper cites MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:41.239460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 38dced97-d1a5-4b6b-b89f-42a72f1fcdac · outbound
This paper cites PointNet: A 3D Convolu- tional Neural Network for real-time object class recognition,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars PointNet: A 3D Convolu- tional Neural Network for real-time object class recognition,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:41.095515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 53d193db-23ba-4da9-9ade-bb686d11cd80 · outbound
This paper cites PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:40.763310Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 22898e50-5f0c-4481-a4de-ae87bc0edab4 · outbound
This paper cites Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:40.512192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 50348f32-cd84-43ca-a575-aad03ccf140a · outbound
This paper cites Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T11:09:38.713756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 13b4449b-7aa4-4176-a791-eeab7fabc0eb · outbound
This paper cites Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:40.321423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7f13d9ce-edec-49ad-89bb-0e841c287620 · outbound
This paper cites KAN: Kolmogorov-Arnold Networks.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars KAN: Kolmogorov-Arnold Networks
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:09:36.864579Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7cb5cd37-f2ba-40c8-82b7-70bb04e1ffc2 · outbound
This paper cites A physics-informed deep convolutional neural network for simulating and predicting transient darcy flows in heterogeneous reservoirs without labeled data,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars A physics-informed deep convolutional neural network for simulating and predicting transient darcy flows in heterogeneous reservoirs without labeled data,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:40.047381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a777a7af-da52-48cd-b1a8-536d5bd34565 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Adam: A Method for Stochastic Optimization
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:09:37.070678Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 66ce328f-cc85-40aa-b4d4-cd39492c2650 · outbound
This paper cites Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:39.809330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ecb740aa-7c26-4971-8bd7-093696d04c14 · outbound
This paper cites Understanding the difficulty of training deep feedforward neural networks,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Understanding the difficulty of training deep feedforward neural networks,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:39.541814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2c5d9780-9172-4989-89e8-feead8cb2339 · outbound
This paper cites On the spectral bias of neural networks,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars On the spectral bias of neural networks,
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:09:37.422269Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ae17346-ed8a-4bab-bd15-7103823f3efc · outbound
This paper cites A comprehensive study of non-adaptive and residual-based adaptive sampling for physics- informed neural networks,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars A comprehensive study of non-adaptive and residual-based adaptive sampling for physics- informed neural networks,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:39.248611Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7aa9a138-0e57-4919-aaf4-b4af5d994ee5 · outbound
This paper cites FourierKAN outperforms MLP on Text Classification Head Fine-tuning.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars FourierKAN outperforms MLP on Text Classification Head Fine-tuning
Reference 43
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T11:09:38.394270Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c780021a-43cd-45c5-8089-0b275e38de7e · outbound
This paper cites Kolmogorov-Arnold Networks are Radial Basis Function Networks.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Kolmogorov-Arnold Networks are Radial Basis Function Networks
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:09:37.948691Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 50949513-c1a2-4683-949d-d0c246ceecec · outbound
This paper cites Physics-informed deep learning for 3D modeling of light diffraction from optical metasurfaces,.
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars Physics-informed deep learning for 3D modeling of light diffraction from optical metasurfaces,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:09:38.960764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.