Paper Citation Record · LEDGER

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts

As of 18 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2412.06842.

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

pith.paper-citation-record.v1
2412.06842 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:30:38.070485Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact7
verified fuzzy15
unresolved13
parse uncertain0
malformed identifier4
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b8f3d55-ac4b-4de7-bc1f-3ebb626704aa · outbound

This paper cites Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems 22 Involving Nonlinear Partial Differential Equations,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems 22 Involving Nonlinear Partial Differential Equations,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:37.887157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6535b806-649d-4da6-a8b1-a1c919859f63 · outbound

This paper cites Physics-Informed Machine Learning,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Physics-Informed Machine Learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:37.892040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6776554-4dee-49f7-8b36-f2b712945d97 · outbound

This paper cites Parallel Physics-Informed Neural Networks via Domain Decomposition,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Parallel Physics-Informed Neural Networks via Domain Decomposition,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.824539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 75936631-f5d4-4bed-a23b-3d344cea1835 · outbound

This paper cites Physics- Informed Neural Networks (PINNs) for Fluid Mechanics: A Review,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Physics- Informed Neural Networks (PINNs) for Fluid Mechanics: A Review,

Reference 4

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T20:30:38.810404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9d34d12b-71c5-49b8-a361-55da35d0bfba · outbound

This paper cites Respecting causality is all you need for training physics-informed neural networks.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Respecting causality is all you need for training physics-informed neural networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:37.906398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cba3410-a991-4c2d-890e-b56e74965eba · outbound

This paper cites Partition of Unity Networks: Deep Hp-Approximation,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Partition of Unity Networks: Deep Hp-Approximation,

Reference 6

Resolution: verified exact; doi, observed 2026-08-11T20:30:38.226280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b3844e5-3426-4f51-b0c3-25520b828a7d · outbound

This paper cites The Partition of Unity Finite Element Method: Basic Theory and Applications,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts The Partition of Unity Finite Element Method: Basic Theory and Applications,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:37.916807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4f9d7d8-4bd9-4747-8c81-b0aa973f9cbc · outbound

This paper cites Probabilistic partition of unity networks: clustering based deep approximation.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Probabilistic partition of unity networks: clustering based deep approximation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:30:38.489864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 90794584-3a7b-40d6-b04f-d98813d81359 · outbound

This paper cites Probabilistic Partition of Unity Networks for High-dimensional Regression Problems,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Probabilistic Partition of Unity Networks for High-dimensional Regression Problems,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.795353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 103989d7-875e-41a2-9636-24416c776a93 · outbound

This paper cites Modeling of One-Dimensional Ablation with Porous Flow Using Finite Control Volume Procedure.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Modeling of One-Dimensional Ablation with Porous Flow Using Finite Control Volume Procedure

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.780145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d89fcff-7aab-43c7-b31e-605201b8af4e · outbound

This paper cites Mesh Deformation Boundary Conditions for Three-Dimensional Ablation Solvers,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Mesh Deformation Boundary Conditions for Three-Dimensional Ablation Solvers,

Reference 11

Resolution: verified exact; doi, observed 2026-08-11T20:30:38.198867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f5fdb15-d18e-4ada-b005-b24fdd45197c · outbound

This paper cites High-Temperature Liquid Metal Infusion Considering Surface Tension- Viscosity Dissipation,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts High-Temperature Liquid Metal Infusion Considering Surface Tension- Viscosity Dissipation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.765060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3ba408d1-e071-429e-8f95-5877cdb0b378 · outbound

This paper cites Sensitivity of Viscosity on Molten Ti Infusion into a B4C- Packed Bed at the Microscale,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Sensitivity of Viscosity on Molten Ti Infusion into a B4C- Packed Bed at the Microscale,

Reference 13

Resolution: verified exact; doi, observed 2026-08-11T20:30:38.183347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b1e9054c-6466-444f-be49-4880cc01bb06 · outbound

This paper cites A., 2010, Three Dimensional Finite Element Ablative Thermal Response Anal- ysis Applied to Heatshield Penetration Design, Georgia Institute of Technology.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts A., 2010, Three Dimensional Finite Element Ablative Thermal Response Anal- ysis Applied to Heatshield Penetration Design, Georgia Institute of Technology

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.748470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 27a37488-51c7-4961-85c0-cc77ae320ea1 · outbound

This paper cites Flow Mechanics in Ablative Thermal Protection Systems,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Flow Mechanics in Ablative Thermal Protection Systems,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:37.954269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee108b18-4da8-460c-8411-959238b90a00 · outbound

This paper cites On the Stability of Combustion and Laser-Produced Ablation Fronts,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts On the Stability of Combustion and Laser-Produced Ablation Fronts,

Reference 16

Resolution: verified exact; doi, observed 2026-08-11T20:30:38.155863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 92cb1e1f-878b-4e79-bfda-01685d2bd4b2 · outbound

This paper cites Rayleigh–Taylor and Richtmyer–Meshkov Instabilities: A Journey through Scales,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Rayleigh–Taylor and Richtmyer–Meshkov Instabilities: A Journey through Scales,

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T20:30:38.468836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 25588dcc-9233-46ae-a223-66adacf72853 · outbound

This paper cites Nonintrusive Manufactured Solutions for Ablation,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Nonintrusive Manufactured Solutions for Ablation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.733543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bfcf21f2-e908-447c-a1b4-90f468027394 · outbound

This paper cites Code Verification by the Method of Manufactured Solutions,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Code Verification by the Method of Manufactured Solutions,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.719674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 418cd263-b00f-462f-a743-bad307f0d236 · outbound

This paper cites an unresolved cited work.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:30:38.705929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d833edba-3b78-46b8-8538-166a3033569c · outbound

This paper cites Albany: Us- ing Component-Based Design to Develop a Flexible, Generic Multiphysics Analysis Code,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Albany: Us- ing Component-Based Design to Develop a Flexible, Generic Multiphysics Analysis Code,

Reference 21

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T20:30:38.691310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cae360f6-b34b-4cd2-a153-110835f0b9a2 · outbound

This paper cites Albany/FELIX: A Parallel, Scalable and Robust, Finite Element, First-Order Stokes Approximation Ice Sheet Solver Built for Advanced Analysis,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Albany/FELIX: A Parallel, Scalable and Robust, Finite Element, First-Order Stokes Approximation Ice Sheet Solver Built for Advanced Analysis,

Reference 22

Resolution: verified exact; doi, observed 2026-08-11T20:30:38.137579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c275b422-f177-4c71-b8ab-6b8c4d736722 · outbound

This paper cites MPAS-Albany Land Ice (MALI): A Variable-Resolution Ice Sheet Model for Earth System Modeling Using Voronoi Grids,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts MPAS-Albany Land Ice (MALI): A Variable-Resolution Ice Sheet Model for Earth System Modeling Using Voronoi Grids,

Reference 23

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T20:30:38.676124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fa09ba86-7325-4486-acc7-c538ede98711 · outbound

This paper cites Verification and Validation in Com- putational Fluid Dynamics,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Verification and Validation in Com- putational Fluid Dynamics,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:37.993152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21dd4205-74f1-4bcf-abeb-d8c91c72929a · outbound

This paper cites L., and Roy, C.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts L., and Roy, C

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.660177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d71177af-d04b-4ed3-a458-4c72f91f3749 · outbound

This paper cites L., and Kutz, J.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts L., and Kutz, J

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.644642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e87d60dc-d790-4ff9-ae14-f092e86b0650 · outbound

This paper cites M., Ebeida, M.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts M., Ebeida, M

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.629559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d8f14f13-b32d-46fd-8eff-e8b13164247c · outbound

This paper cites M., Bohnhoff, W.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts M., Bohnhoff, W

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.614441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4fb272aa-6b4f-42c7-b5ec-fa62e6eef520 · outbound

This paper cites Parameter Sensitivity and Statistical Cor- relation Found in Atmospheric Turbulence Studies,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Parameter Sensitivity and Statistical Cor- relation Found in Atmospheric Turbulence Studies,

Reference 29

Resolution: verified exact; doi, observed 2026-08-11T20:30:38.109409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c89a9be4-dba3-44aa-8703-ae8992d61b26 · outbound

This paper cites Mesh Adaptability Technique for Canonical Turbulent Jet Flows via Reinforcement Learning,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Mesh Adaptability Technique for Canonical Turbulent Jet Flows via Reinforcement Learning,

Reference 30

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T20:30:38.600269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 689c23e6-1495-4d8f-9ea2-86c8058cbf89 · outbound

This paper cites an unresolved cited work.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:30:38.584204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f37394b4-0f00-42f2-9c4a-7487d50584d0 · outbound

This paper cites Forward and Inverse Modeling of Ice Sheet Flow Using Physics-informed Neural Networks: Application to Helheim Glacier, Greenland,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Forward and Inverse Modeling of Ice Sheet Flow Using Physics-informed Neural Networks: Application to Helheim Glacier, Greenland,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.569907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb8f1a63-fd53-4574-8969-e24f0b0335d3 · outbound

This paper cites Discovering the Rheology of Antarc- tic Ice Shelves via Physics-Informed Deep Learning.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Discovering the Rheology of Antarc- tic Ice Shelves via Physics-Informed Deep Learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.554757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a4300ea8-d356-4974-8ca9-3293f6a2eeee · outbound

This paper cites One-Dimensional Ice Shelf Hardness Inversion: Clus- tering Behavior and Collocation Resampling in Physics-Informed Neural Networks,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts One-Dimensional Ice Shelf Hardness Inversion: Clus- tering Behavior and Collocation Resampling in Physics-Informed Neural Networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.538941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b7e7341-8e05-499b-9685-ca465f28a593 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Adam: A Method for Stochastic Optimization

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:38.045490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b14a9e35-1415-4917-960d-de8f4917fe3f · outbound

This paper cites Activation Functions: Comparison of trends in Practice and Research for Deep Learning.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Activation Functions: Comparison of trends in Practice and Research for Deep Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:38.050674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f9de46c-995f-4d9a-aac1-7f8407a3acb3 · outbound

This paper cites On Model Stability as a Function of Random Seed.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts On Model Stability as a Function of Random Seed

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:38.055773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 029ecd89-ff81-4bdf-9b3b-3c30d040a70a · outbound

This paper cites L2 Regularization for Learning Kernels.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts L2 Regularization for Learning Kernels

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:38.060800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fa8503a-3b0f-4b90-8015-d1ef96003f8c · outbound

This paper cites L2 Regularization versus Batch and Weight Normalization.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts L2 Regularization versus Batch and Weight Normalization

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T20:30:38.065681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b227cb9-7c82-4bd9-947f-ab674b30bcfd · outbound

This paper cites Understanding the Difficulty of Training Deep Feed- forward Neural Networks,.

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts Understanding the Difficulty of Training Deep Feed- forward Neural Networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:30:38.522513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.