Paper Citation Record · LEDGER

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design

As of 18 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 2 inbound Pith citation observations for arXiv:2505.20300.

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

pith.paper-citation-record.v1
2505.20300 v1

Coverage vector

measured 81 of 81 reference resolution

Typed states for the displayed outbound observations.

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measured 83 of 83 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:51:35.042258Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T01:58:51.556380Z

Reference resolution

81 of 81 outbound references displayed

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External citation measurements

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Outbound references

Observation 74e0cb1c-f19a-42da-973d-0b37ef84286b · outbound

This paper cites Systematic design of chemical reactors with multiple stages via multi-objective optimization approach.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Systematic design of chemical reactors with multiple stages via multi-objective optimization approach

Reference 1

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Observation d745deed-da61-4c72-baa5-7ef644f33262 · outbound

This paper cites Fundamentals of green chemistry: efficiency in reaction design.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Fundamentals of green chemistry: efficiency in reaction design

Reference 2

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Observation 45297fe3-da93-4d07-a80b-767d3fcfbb92 · outbound

This paper cites Chemical reactor analysis and design.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Chemical reactor analysis and design

Reference 3

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Observation a1b33b13-2be4-4cac-bf06-7358b021d734 · outbound

This paper cites Chemical engineering design: principles, practice and eco- nomics of plant and process design.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Chemical engineering design: principles, practice and eco- nomics of plant and process design

Reference 4

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Observation 0f7a07f1-1248-4ad4-a412-04375ea9c845 · outbound

This paper cites Thermal safety of chemical processes: risk assessment and process design.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Thermal safety of chemical processes: risk assessment and process design

Reference 5

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Observation 5362b6ef-897c-44db-814b-a54dc12445a9 · outbound

This paper cites Essentials of chemical reaction engineering: essenti chemica reactio engi.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Essentials of chemical reaction engineering: essenti chemica reactio engi

Reference 6

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Observation 7edcbe78-6a7a-45f6-b698-084f09df642d · outbound

This paper cites Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors

Reference 7

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Observation 86c6c0d7-fb38-4812-b4db-c91b8d814692 · outbound

This paper cites Advances of machine learning in molecular modeling and simulation.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Advances of machine learning in molecular modeling and simulation

Reference 8

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Observation 677604b1-69e3-4733-a881-45a5b74bd25a · outbound

This paper cites The appli- cation of physics-informed machine learning in multiphysics modeling in chemical engineering.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design The appli- cation of physics-informed machine learning in multiphysics modeling in chemical engineering

Reference 9

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Observation c54744d4-6c6b-4084-94e4-cfaefd9059ec · outbound

This paper cites Combining cfd and ai/ml modeling to improve the performance of polypropy- lene fluidized bed reactors.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Combining cfd and ai/ml modeling to improve the performance of polypropy- lene fluidized bed reactors

Reference 10

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Observation 1bbaad56-3927-4484-98f7-45ff1a04fa1f · outbound

This paper cites Analysis and predic- tion of hematocrit in microvascular networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Analysis and predic- tion of hematocrit in microvascular networks

Reference 11

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Observation 02d1df4a-36b5-4b41-8fe6-238fa1d98b7d · outbound

This paper cites Laplace neural operator for solving differential equations.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Laplace neural operator for solving differential equations

Reference 12

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Learning the solution operator of para- metric partial differential equations with physics-informed deeponets

Reference 13

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Observation cb0f3611-0718-423d-9475-f721b0a9cb26 · outbound

This paper cites Physics-informed machine learning.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed machine learning

Reference 14

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Observation 506d879b-a56a-4625-b6cc-a8f4bd9bc017 · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 15

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Observation 60df5078-ea51-4b07-bcc3-762b9e485af4 · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Deepxde: A deep learning library for solving differential equations

Reference 16

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Observation 994f87f7-180a-40df-8c41-ea2533435cdd · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Scientific machine learning through physics–informed neural networks: Where we are and what’s next

Reference 17

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Observation 4b2c559b-a6bf-404c-9397-f4878c2ff811 · outbound

This paper cites From pinns to pikans: Recent advances in physics-informed machine learning.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design From pinns to pikans: Recent advances in physics-informed machine learning

Reference 18

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Observation 506b232a-6e06-4382-8254-871527fddc24 · outbound

This paper cites Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications

Reference 19

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed neural networks for inverse problems in nano-optics and metamaterials

Reference 20

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Observation 5fd25595-308d-4252-bf6a-1c777474c2e2 · outbound

This paper cites Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance

Reference 21

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Observation 2dd38f99-9a5f-42ff-aeb2-a812b63743b4 · outbound

This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Reference 22

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FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed neural networks (pinns) for fluid mechanics: A review

Reference 23

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This paper cites Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning

Reference 24

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FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A review of physics- informed machine learning in fluid mechanics

Reference 25

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Systems biology informed deep learning for inferring parameters and hidden dynamics

Reference 26

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FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Systems biology: Iden- tifiability analysis and parameter identification via systems-biology-informed neural networks

Reference 27

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Observation 560f4cc6-b183-417b-991e-003c8a6e5fdd · outbound

This paper cites Investigating molecular transport in the human brain from mri with physics-informed neural networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Investigating molecular transport in the human brain from mri with physics-informed neural networks

Reference 28

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Observation bcdc9a28-12ab-47af-85aa-3d84a94ae09a · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed neural networks with hard constraints for inverse design

Reference 29

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This paper cites Digital twin of optical networks: a review of recent advances and future trends.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Digital twin of optical networks: a review of recent advances and future trends

Reference 30

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Observation d739e6a8-d2b4-4ef4-abee-82dbe328884e · outbound

This paper cites Data-driven physics-informed neural networks: A digital twin perspective.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Data-driven physics-informed neural networks: A digital twin perspective

Reference 31

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Observation ab762276-13a4-47f6-85e9-4d424aa4d983 · outbound

This paper cites Self-adaptive physics-driven deep learning for seismic wave modeling in complex topography.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Self-adaptive physics-driven deep learning for seismic wave modeling in complex topography

Reference 32

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Observation ffca48e5-9847-46ef-a41e-4cfcb3b03b21 · outbound

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FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Optimal temperature trajectory for tubular reactor using physics informed neural networks

Reference 33

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This paper cites Data-driven discovery of reaction kinetic models in dynamic plug flow reactors using symbolic regression.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Data-driven discovery of reaction kinetic models in dynamic plug flow reactors using symbolic regression

Reference 34

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This paper cites Physics-informed deep learning for data-driven solutions of computational fluid dynamics.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed deep learning for data-driven solutions of computational fluid dynamics

Reference 35

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FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Unit operation and process modeling with physics-informed machine learning

Reference 36

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Observation d82f48ec-fee0-4f2b-b6c5-4c980aeb721b · outbound

This paper cites Physics-informed neural networks for phase-field method in two-phase flow.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed neural networks for phase-field method in two-phase flow

Reference 37

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Observation 19f49e5e-c04d-4279-a931-6ae9c030bd01 · outbound

This paper cites Physics-informed neural networks and time-series transformer for modeling of chemical reactors.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed neural networks and time-series transformer for modeling of chemical reactors

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.347739Z

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Observation 2a5d56e1-cfbc-4356-ba4c-4ac61f1bbd70 · outbound

This paper cites Physics-informed learning of chemical reactor systems using decoupling–coupling train- ing framework.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed learning of chemical reactor systems using decoupling–coupling train- ing framework

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.333348Z

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Observation 86224ca4-a825-4e69-a8a5-977143403aa3 · outbound

This paper cites Physics informed neural network for forward and inverse multispecies contaminant transport with variable pa- rameters.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics informed neural network for forward and inverse multispecies contaminant transport with variable pa- rameters

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.318213Z

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Observation 2af6f8b5-a731-452b-882d-022c5e5411c8 · outbound

This paper cites A physics-informed neural net- work based simulation tool for reacting flow with multicomponent reactants.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A physics-informed neural net- work based simulation tool for reacting flow with multicomponent reactants

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.301445Z

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Observation 784f0510-f536-4f93-a9db-a2a8dbf8442c · outbound

This paper cites Physics-informed graph convolutional neural network for modeling fluid flow and heat con- vection.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed graph convolutional neural network for modeling fluid flow and heat con- vection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.285930Z

Source-reported events for the cited work

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Observation 4f0f3311-2cc7-40f1-b9de-97fb6416f1bd · outbound

This paper cites Simulation of multi-species flow and heat transfer using physics-informed neural networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Simulation of multi-species flow and heat transfer using physics-informed neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.269670Z

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Observation bae0cffd-abd9-4583-9348-1bf30cb1eaab · outbound

This paper cites Advancement of machine learning in materials science.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Advancement of machine learning in materials science

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.255029Z

Source-reported events for the cited work

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Observation 39d0e294-f3a2-4104-b31c-92983b701ae9 · outbound

This paper cites Machine learning for fluid mechanics.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Machine learning for fluid mechanics

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.240209Z

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Observation 32dc7ef5-67d4-4dd1-8855-67bf987e92b6 · outbound

This paper cites Machine learning in materials science.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Machine learning in materials science

Reference 46

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This paper cites Machine learning in medicine: a practical introduction.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Machine learning in medicine: a practical introduction

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.216363Z

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Observation 1bb64f88-b013-407c-9ba1-1c01eb62ebcd · outbound

This paper cites Gpt vs human for scientific reviews: A dual source review on applications of chatgpt in science.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Gpt vs human for scientific reviews: A dual source review on applications of chatgpt in science

Reference 48

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Observation b3623f4f-04e0-49d1-b47b-93db8bbbf155 · outbound

This paper cites Forward physics-informed neural networks suitable for multiple operating conditions of catalytic co2 methanation isothermal fixed-bed.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Forward physics-informed neural networks suitable for multiple operating conditions of catalytic co2 methanation isothermal fixed-bed

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.076580Z

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 2467a1de-1699-4f78-b98d-d40bfa9153d1 · outbound

This paper cites ¨Uber die reaktionsgeschwindigkeit bei der inversion von rohrzucker durch s¨ auren.Zeitschrift f¨ ur physikalische Chemie, 4(1):226–248, 1889.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design ¨Uber die reaktionsgeschwindigkeit bei der inversion von rohrzucker durch s¨ auren.Zeitschrift f¨ ur physikalische Chemie, 4(1):226–248, 1889

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.062926Z

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 5310d57e-c7a2-4b33-a61b-eee47553cbd9 · outbound

This paper cites A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.689738Z

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Observation 0943fa73-0f07-4474-bdd4-44f03894a2c2 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Multilayer feedforward networks are universal approximators

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.694113Z

Source-reported events for the cited work

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Observation 5e952f2f-1d2f-425c-b710-c67f14f1fc9b · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design KAN: Kolmogorov-Arnold Networks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.698648Z

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Observation 5f1d5745-1d27-4578-9cc1-5d0587b9eb8d · outbound

This paper cites KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.703419Z

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Observation faf5ef92-71c8-4c41-9f6f-950628e0e7aa · outbound

This paper cites Weight normalization: A simple reparameterization to accelerate training of deep neural networks.Advances in neural information processing systems, 29, 2016.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Weight normalization: A simple reparameterization to accelerate training of deep neural networks.Advances in neural information processing systems, 29, 2016

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.708544Z

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Observation faab9820-efe4-4606-a7d5-bfee7bd8d412 · outbound

This paper cites Residual-based attention in physics-informed neural networks.Computer Methods in Applied Mechanics and Engineering , 421:116805, 2024.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Residual-based attention in physics-informed neural networks.Computer Methods in Applied Mechanics and Engineering , 421:116805, 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:56.029023Z

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 abc6253a-9fa7-41cd-95f9-9322aad14de7 · outbound

This paper cites A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.717321Z

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Observation b2de81ca-6518-4489-bc09-92e408c74cec · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Understanding and mitigating gradient flow pathologies in physics-informed neural networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.721467Z

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Observation cd5b09bd-0a3f-4de1-8375-e88f028dc6d0 · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.734194Z

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Observation d699df4c-b956-4f24-af20-b0246419aee9 · outbound

This paper cites Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.932133Z

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 093d4324-0a2e-4eeb-a1d5-6a9ace6f6941 · outbound

This paper cites Physics-informed neural networks for high-speed flows.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Physics-informed neural networks for high-speed flows

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.789427Z

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Observation 6bd1b83f-eb29-4ba1-8a2d-73b56a96958f · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.811762Z

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Unavailable: canonical work link unavailable.

Observation e91fa1bd-1272-45df-89ab-88ce5b703e12 · outbound

This paper cites Self-adaptive physics-informed neural networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Self-adaptive physics-informed neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.781005Z

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 59b0567f-acb6-46dc-8846-4eec532df011 · outbound

This paper cites Learning in PINNs: Phase transition, total diffusion, and generalization.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Learning in PINNs: Phase transition, total diffusion, and generalization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.820468Z

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Observation e79aada0-54ae-4f39-9165-dfd52fdf25f1 · outbound

This paper cites Self-adaptive loss balanced physics-informed neural networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Self-adaptive loss balanced physics-informed neural networks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.825603Z

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Observation c1ec9504-f562-40bf-9462-7671a1d547ed · outbound

This paper cites A dual-dimer method for training physics-constrained neural networks with minimax architecture.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A dual-dimer method for training physics-constrained neural networks with minimax architecture

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.647859Z

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 283e3bf9-f73b-4def-a320-a719682f2483 · outbound

This paper cites Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs).

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.834542Z

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Observation 23eed3a9-1ff0-4fd2-9652-24560fd1cd39 · outbound

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

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Respecting causality is all you need for training physics-informed neural networks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.839600Z

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Observation 7c5889c5-8259-4b79-b7e6-f966a51610e8 · outbound

This paper cites When and why PINNs fail to train: A neural tangent kernel perspective.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design When and why PINNs fail to train: A neural tangent kernel perspective

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.843661Z

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Observation 89056746-c031-4aba-be5d-97cef96a106c · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Characterizing possible failure modes in physics-informed neural networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.848756Z

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Observation f0893916-0e3f-4579-a843-0d2bf487b24a · outbound

This paper cites A Meshless Solver for Blood Flow Simula- tions in Elastic Vessels Using a Physics-Informed Neural Network.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design A Meshless Solver for Blood Flow Simula- tions in Elastic Vessels Using a Physics-Informed Neural Network

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.540429Z

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 db57f35b-a223-4d65-aaa5-40dfc5f723fd · outbound

This paper cites An Expert's Guide to Training Physics-informed Neural Networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design An Expert's Guide to Training Physics-informed Neural Networks

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.857358Z

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Observation b7083b71-9065-43cf-a68d-5e2f628fb41c · outbound

This paper cites PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:54.899644Z

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Observation 8ecd5756-cd96-4a23-975b-220fd0b9b63a · outbound

This paper cites AI-Aristotle: A physics-informed framework for systems biology gray-box identification.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design AI-Aristotle: A physics-informed framework for systems biology gray-box identification

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.523799Z

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 3d17ae09-5f59-44eb-9b97-c62f33cfbb4b · outbound

This paper cites Cminns: Compart- ment model informed neural networks—unlocking drug dynamics.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Cminns: Compart- ment model informed neural networks—unlocking drug dynamics

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.456848Z

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 c7b47e57-4aab-49d0-b235-3b161ed831f4 · outbound

This paper cites Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.325947Z

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 28e1d889-2ffd-4d8f-a8b8-485b28defdc7 · outbound

This paper cites Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:55.045665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 370834bf-0336-4092-84b5-0107e4e3c20f · outbound

This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Challenges in Training PINNs: A Loss Landscape Perspective

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:55.050048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 248b9932-5e83-421c-8347-40c6b3e5c21e · outbound

This paper cites an unresolved cited work.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:48:55.311056Z

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 6d48005e-9e32-4138-9c5b-cdb780772541 · outbound

This paper cites Smith, Hong Zhang, et al.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Smith, Hong Zhang, et al

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:48:55.296768Z

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 29e1491e-f753-4637-8605-580c8a330a67 · outbound

This paper cites an unresolved cited work.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:48:55.282454Z

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

Observation 96aead72-42d9-4876-bb5a-f89a162c9dff · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:58:51.558757Z

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 a505aaa4-9406-49af-85b9-25b1e8d9bd95 · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T18:51:35.042258Z

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Unavailable: canonical work link unavailable.