Paper Citation Record · LEDGER

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks

As of 11 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 2 inbound Pith citation observations for arXiv:2501.07765.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.07765 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:41:44.693008Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T14:11:56.439388Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

46 of 46 outbound references displayed

verified exact2
verified fuzzy29
unresolved15
parse uncertain0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 3f174481-967b-4964-8115-25b0fbe57f8e · outbound

This paper cites Machine learning and big scientific data.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Machine learning and big scientific data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.443284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0a691d0e-8848-49e9-babe-fe79a4d192c6 · outbound

This paper cites Machine learning for science: state of the art and future prospects.science, 293(5537):2051–2055, 2001.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Machine learning for science: state of the art and future prospects.science, 293(5537):2051–2055, 2001

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.429304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2d2889ea-2aac-46b6-9c16-cc1a63ea24cb · outbound

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

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.415633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f056ff42-f3d7-4274-b412-5378a9dba1a4 · outbound

This paper cites Learning Mesh-Based Simulation with Graph Networks.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Learning Mesh-Based Simulation with Graph Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.520246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 178da6d9-f17c-4d05-a29c-8bfd30c2df4d · outbound

This paper cites Learningnonlinearoperators viadeeponetbasedontheuniversalapproximationtheoremofoperators.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Learningnonlinearoperators viadeeponetbasedontheuniversalapproximationtheoremofoperators

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.402307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2acbe5c6-ce35-4450-a74c-b4ec580306e4 · outbound

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

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Fourier Neural Operator for Parametric Partial Differential Equations

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.529694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fb81127-ed79-4e6b-86a2-beaee1ab49cf · outbound

This paper cites Solving the wave equation with physics-informed deep learning.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Solving the wave equation with physics-informed deep learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.535137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25416a1e-03d8-4c66-abef-76bc9dc1bbb4 · outbound

This paper cites Physics-informedneural networks (pinns) for fluid mechanics: A review.Acta Mechanica Sinica, 37(12):1727–1738, 2021.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Physics-informedneural networks (pinns) for fluid mechanics: A review.Acta Mechanica Sinica, 37(12):1727–1738, 2021

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.388506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d6bac8a9-7c46-4d45-9ec5-cea9f7494da8 · outbound

This paper cites Nsfnets(navier-stokesflownets): Physics-informed neural networks for the incompressible navier-stokes equations.Journal of Computational Physics, 426:109951, 2021.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Nsfnets(navier-stokesflownets): Physics-informed neural networks for the incompressible navier-stokes equations.Journal of Computational Physics, 426:109951, 2021

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.374978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 95acd3d9-6351-4bae-a9c9-f62721b25086 · outbound

This paper cites Physics-informed neural networks for power systems.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Physics-informed neural networks for power systems

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.361968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation acf27ebc-c298-4280-9967-1be748ac1c46 · outbound

This paper cites Predictionofporousmediafluidflowusingphysicsinformed neural networks.Journal of Petroleum Science and Engineering, 208:109205, 2022.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Predictionofporousmediafluidflowusingphysicsinformed neural networks.Journal of Petroleum Science and Engineering, 208:109205, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.347926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be70aec6-269c-407b-b9c0-025d7cf2607f · outbound

This paper cites Deep learning of two-phase flow in porous media via theory-guided neural networks.SPE Journal, 27(02):1176–1194, 2022.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Deep learning of two-phase flow in porous media via theory-guided neural networks.SPE Journal, 27(02):1176–1194, 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.334527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0541a9db-2178-4921-a4ed-2d7bfe5c6667 · outbound

This paper cites A unified deep artificial neural network approach to partial differential equations in complex geometries.Neurocomputing, 317:28–41, 2018.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks A unified deep artificial neural network approach to partial differential equations in complex geometries.Neurocomputing, 317:28–41, 2018

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.560725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a2b4143-a05d-4015-bd49-bb673aed2d8a · outbound

This paper cites an unresolved cited work.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.564933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14fb4819-5152-4b4b-9265-72b3b65d710f · outbound

This paper cites Dgm: A deep learning algorithm for solving partial differential equations.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Dgm: A deep learning algorithm for solving partial differential equations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.569094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0dd974f-146d-4272-aad4-179c8891161c · outbound

This paper cites The deep ritz method: A deep learning-based numerical algorithm for solving variational problems.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks The deep ritz method: A deep learning-based numerical algorithm for solving variational problems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.292389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0cdd90b0-dd5c-4eb6-8702-68632fb30309 · outbound

This paper cites Solving high-dimensional partial differential equations using deep learning.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Solving high-dimensional partial differential equations using deep learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.577230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2914723f-368a-458c-99d0-b2200d28bf9a · outbound

This paper cites Variational Physics-Informed Neural Networks For Solving Partial Differential Equations.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Variational Physics-Informed Neural Networks For Solving Partial Differential Equations

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.581231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f244719e-8cbb-4d3a-8d6e-1f71736a5266 · outbound

This paper cites Automatic differentiation in machine learning: a survey.Journal of machine learning research, 18(153):1–43, 2018.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Automatic differentiation in machine learning: a survey.Journal of machine learning research, 18(153):1–43, 2018

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.585549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 147ca91e-40c8-4231-aa65-1fecce12862c · outbound

This paper cites Stochastic gradient learning in neural networks.Proceedings of Neuro-Nımes, 91(8):12, 1991.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Stochastic gradient learning in neural networks.Proceedings of Neuro-Nımes, 91(8):12, 1991

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.257879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 97b2b75d-626b-44b5-b79c-61ed6cea1ac2 · outbound

This paper cites A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:41:44.769245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c8fc248b-1169-4518-80f4-0b3ec6611a24 · outbound

This paper cites A comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks A comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:41:44.749216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8aaca576-f853-4622-aec9-17adf0461a73 · outbound

This paper cites Int-deep: A deep learning initialized iterative method for nonlinear problems.Journal of computational physics, 419:109675, 2020.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Int-deep: A deep learning initialized iterative method for nonlinear problems.Journal of computational physics, 419:109675, 2020

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.240852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c51e274c-e3e6-41b5-8a8d-e73bde08f635 · outbound

This paper cites Automatically imposing boundary conditions for boundary valueproblemsbyunifiedphysics-informedneuralnetwork.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Automatically imposing boundary conditions for boundary valueproblemsbyunifiedphysics-informedneuralnetwork

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.226566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 19f677ec-6a90-48a2-a753-384f6cdc53dd · outbound

This paper cites an unresolved cited work.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:41:45.211958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6170d6f-1575-4463-a9e2-84a90d58781c · outbound

This paper cites The deep ritz method: a deep learning-based numerical algorithm for solving variational problems.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks The deep ritz method: a deep learning-based numerical algorithm for solving variational problems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.198008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f8f8dee4-bb0a-4f6d-9c97-c410de67000c · outbound

This paper cites Anenergyapproachtothesolutionofpartialdifferentialequations incomputationalmechanicsviamachinelearning: Concepts,implementationandapplications.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Anenergyapproachtothesolutionofpartialdifferentialequations incomputationalmechanicsviamachinelearning: Concepts,implementationandapplications

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.183963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3d7b6820-37da-459d-b0e6-ccb6f30007c3 · outbound

This paper cites A deep energy method for finite deformation hyperelasticity.European Journal of Mechanics-A/Solids, 80:103874, 2020.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks A deep energy method for finite deformation hyperelasticity.European Journal of Mechanics-A/Solids, 80:103874, 2020

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.620309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5dd17da-42c0-47be-9353-c4c5a97bb020 · outbound

This paper cites Parametric deepenergyapproachforelasticityaccountingforstraingradienteffects.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Parametric deepenergyapproachforelasticityaccountingforstraingradienteffects

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.160899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation eb3451e9-2fa1-4bb7-b955-f7c1a6f49427 · outbound

This paper cites an unresolved cited work.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:41:45.148554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 58851300-a39d-48c3-8bf6-cd49413e88ef · outbound

This paper cites A coupled finite element-element-free galerkin method.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks A coupled finite element-element-free galerkin method

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.135600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 862a2edc-d634-460f-a990-dc319044711c · outbound

This paper cites Enrichment and coupling of the finite element and meshless methods.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Enrichment and coupling of the finite element and meshless methods

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.122019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 76bde138-9c86-4527-a49f-1c05c002c47e · outbound

This paper cites Hierarchical enrichment for bridging scales and mesh-free boundary conditions.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Hierarchical enrichment for bridging scales and mesh-free boundary conditions

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.106459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b5c7fb91-a390-4b06-8b5b-9d00906d6037 · outbound

This paper cites Element-free galerkin methods in combination with finite element approaches.Computer Methods in Applied Mechanics and Engineering, 135(1-2):143–166, 1996.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Element-free galerkin methods in combination with finite element approaches.Computer Methods in Applied Mechanics and Engineering, 135(1-2):143–166, 1996

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.092419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6d6b60a4-d989-4710-a939-5378ec832945 · outbound

This paper cites Hybrid fem-nn models: Combining artificial neural networks with the finite element method.Journal of Computational Physics, 446:110651, 2021.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Hybrid fem-nn models: Combining artificial neural networks with the finite element method.Journal of Computational Physics, 446:110651, 2021

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.078222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b0cb7020-9da4-4379-88ba-b5443c593659 · outbound

This paper cites Enforcement of essential boundary conditions in meshless approximations using finite elements.Computer Methods in Applied Mechanics and Engineering, 131(1-2):133–145, 1996.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Enforcement of essential boundary conditions in meshless approximations using finite elements.Computer Methods in Applied Mechanics and Engineering, 131(1-2):133–145, 1996

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.063642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 010567b7-36fe-4e65-81eb-01fc9f754771 · outbound

This paper cites Efficient physics informed neural networks coupled with domain decomposition methods for solving coupled multi-physics problems.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Efficient physics informed neural networks coupled with domain decomposition methods for solving coupled multi-physics problems

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.049321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 443c3fdb-b658-4bc6-aa0a-bf208ba32836 · outbound

This paper cites A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics.Computer Methods in Applied Mechanics and Engineering, 379:113741, 2021.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics.Computer Methods in Applied Mechanics and Engineering, 379:113741, 2021

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.658696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58e7f2f0-fead-41f7-90e7-4da98ec182aa · outbound

This paper cites Butterworth-Heinemann, 2013.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Butterworth-Heinemann, 2013

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:45.025412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2c8df977-a6e2-4111-b062-9e9bd6e11657 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and semantics.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.666912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63625e1f-56b2-4760-a970-5bf4b34d4634 · outbound

This paper cites Multi-fidelity physics-constrained neural network and its application in materials modeling.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Multi-fidelity physics-constrained neural network and its application in materials modeling

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:44.999163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 69327420-fba5-46c6-9ed7-441364a25f46 · outbound

This paper cites Understanding and mitigating gradient pathologies in physics-informed neural networks.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T20:41:44.675268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08d495b7-4b05-44de-8a9c-8f8679173651 · outbound

This paper cites Courier Corporation, 2012.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Courier Corporation, 2012

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:44.984235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c7328f0e-6928-4bcf-bcb8-885006209d24 · 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.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks 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 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:44.971088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 370b699e-4f8c-4fa3-9548-4a13cb5b334a · outbound

This paper cites A three-dimensional finite element mesh generator with built-in pre-and post-processing facilities.International Journal for Numerical Methods in Engineering, 11:79, 2020.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks A three-dimensional finite element mesh generator with built-in pre-and post-processing facilities.International Journal for Numerical Methods in Engineering, 11:79, 2020

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:44.957752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8c2b01d4-aeca-435c-95a4-12a78ca33488 · outbound

This paper cites Elasticity theory.M: Science, 1975.

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks Elasticity theory.M: Science, 1975

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:41:44.944570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 78b8e6a8-e3d1-46f4-b3c6-560151a12d9c · inbound

Phy2-ExposNet: A Physics-Informed Neural Network for EMF Exposure Mapping in Complex Urban Environments cites this paper.

Phy2-ExposNet: A Physics-Informed Neural Network for EMF Exposure Mapping in Complex Urban Environments PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:41:31.794410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 96707540-c6dd-48cf-9ca4-61e5ad383195 · inbound

Exact Boundary Enforcement Along Implicit Geometries for Physics-Informed, Deep Learning Problems in Continuum Mechanics cites this paper.

Exact Boundary Enforcement Along Implicit Geometries for Physics-Informed, Deep Learning Problems in Continuum Mechanics PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:13:29.685868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.