Paper Citation Record · LEDGER

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion

As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2502.01820.

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

pith.paper-citation-record.v1
2502.01820 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:24:43.399654Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T14:24:48.666197Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T14:26:28.431385Z

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ec104601-58d9-406e-8804-14efc2aa246d · outbound

This paper cites Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using pod- based reduced-order modeling.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using pod- based reduced-order modeling

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.670720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3d3c8cc0-d196-4ac1-ae48-d0775d09b3f1 · outbound

This paper cites Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-09T14:24:43.437588Z

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Observation 72b936c7-8641-4f43-9755-933e80a64f0e · outbound

This paper cites Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.663600Z

Source-reported events for the cited work

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Observation 726a917b-6975-4b7e-98ff-00a72ea0e080 · outbound

This paper cites Capturing local temperature evolution during additive manufacturing through fourier neural operators.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Capturing local temperature evolution during additive manufacturing through fourier neural operators

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.656743Z

Source-reported events for the cited work

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Observation 2633a576-c40d-4414-9c12-468fd89a6cc0 · outbound

This paper cites A deeponet multi-fidelity approach for residual learning in reduced order modeling.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion A deeponet multi-fidelity approach for residual learning in reduced order modeling

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.649859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 33b87b9a-8dac-467f-b84e-1c30926871cf · outbound

This paper cites Reduced order modeling via pgd for highly transient thermal evolutions in additive manufacturing.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Reduced order modeling via pgd for highly transient thermal evolutions in additive manufacturing

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.642650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e236b342-b2ee-4a51-ac90-85bcd83bcbcb · outbound

This paper cites Toolpath generation for the manufacture of metallic components by means of the laser metal deposition technique.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Toolpath generation for the manufacture of metallic components by means of the laser metal deposition technique

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.635306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 775ccc10-2b90-4300-ba69-65b1de447f07 · outbound

This paper cites On ther- mal modeling of additive manufacturing processes.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion On ther- mal modeling of additive manufacturing processes

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.627866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11b0b192-0ba1-4a78-b909-36a858ee19c9 · outbound

This paper cites A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized pdes.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized pdes

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.620613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f90e79c-4330-45b6-a9ae-bd31396cb076 · outbound

This paper cites En-deeponet: An enrichment ap- proach for enhancing the expressivity of neural operators with applications to seismology.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion En-deeponet: An enrichment ap- proach for enhancing the expressivity of neural operators with applications to seismology

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.613255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1fafb48-8663-4f08-993a-e45b5c3518d7 · outbound

This paper cites Single-track thermal analysis of laser powder bed fusion process: Parametric solution through physics-informed neural networks.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Single-track thermal analysis of laser powder bed fusion process: Parametric solution through physics-informed neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.605689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 08230cf0-b72f-4769-9a9a-b90b87d5d72b · outbound

This paper cites Enhancing pinns for solving pdes via adaptive collocation point movement and adaptive loss weighting.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Enhancing pinns for solving pdes via adaptive collocation point movement and adaptive loss weighting

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.597565Z

Source-reported events for the cited work

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Observation 0d8d28f5-7e2d-4c3f-8cde-6f5e0d86cb08 · outbound

This paper cites Tool path optimization of selective laser sintering processes using deep learning.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Tool path optimization of selective laser sintering processes using deep learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.589894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e7049b99-1f57-48ca-bca9-6f7b527590d0 · outbound

This paper cites An efficient and high- fidelity local multi-mesh finite volume method for heat transfer and fluid flow problems in metal additive manufacturing.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion An efficient and high- fidelity local multi-mesh finite volume method for heat transfer and fluid flow problems in metal additive manufacturing

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.582220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 146eea3b-2a44-4209-bc6c-df686a751625 · outbound

This paper cites A physics- informed neural network framework to predict 3d temperature field without labeled data in process of laser metal deposition.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion A physics- informed neural network framework to predict 3d temperature field without labeled data in process of laser metal deposition

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.574630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e8ce358-72cf-44e1-9d8d-530270f0063c · outbound

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

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Fourier Neural Operator for Parametric Partial Differential Equations

Reference 16

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Source-reported events for the cited work

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Observation 6e95a35f-7e74-4283-92d3-7056da874b45 · outbound

This paper cites Hy- brid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Hy- brid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.566705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b731018-fc29-42ce-9229-4dc09071a5a2 · outbound

This paper cites On the limited memory bfgs method for large scale optimization.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion On the limited memory bfgs method for large scale optimization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T14:24:43.355135Z

Source-reported events for the cited work

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Observation 104f1b5a-7815-4a6b-90b0-f66e8bde1b1e · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T14:24:43.357563Z

Source-reported events for the cited work

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Observation 278cbe21-c45b-4d0c-8530-4924ed29c656 · outbound

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

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Physics-informed neural networks for high-speed flows

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T14:24:43.359968Z

Source-reported events for the cited work

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Observation cd9a420a-b460-4073-b243-97deaa207caa · outbound

This paper cites Multiscale modeling of powder bed–based additive manufac- turing.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Multiscale modeling of powder bed–based additive manufac- turing

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.547111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 731d02ba-d021-4e06-b1aa-dacd9cc35ad4 · outbound

This paper cites Modeling parametric uncertainty in pdes models via physics-informed neural networks.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Modeling parametric uncertainty in pdes models via physics-informed neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.539703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a06e7875-8a2e-4bc2-a4bd-565d92623ea1 · outbound

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

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T14:24:43.367277Z

Source-reported events for the cited work

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Observation ce4e9284-37e8-476a-a41e-8278ea4f42e8 · outbound

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

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Challenges in Training PINNs: A Loss Landscape Perspective

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T14:24:43.369780Z

Source-reported events for the cited work

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Observation 3c0c7d89-2a06-46d9-a587-5986cc4e58c5 · outbound

This paper cites Reduced and all-at-once approaches for model calibration and discovery in computational solid mechanics.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Reduced and all-at-once approaches for model calibration and discovery in computational solid mechanics

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.528507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c5f5d1c0-125f-4efa-92b9-01885c1f7c2b · outbound

This paper cites Advances in computational modeling for laser powder bed fusion additive manufacturing: A comprehensive review of finite element techniques and strategies.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Advances in computational modeling for laser powder bed fusion additive manufacturing: A comprehensive review of finite element techniques and strategies

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.521331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fbc6c769-43b2-4f27-97cf-1f27528cbb72 · outbound

This paper cites Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.514054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f51836d5-7a4f-47e5-b645-5315d95ec3f4 · outbound

This paper cites On the distribution of points in a cube and the approximate evaluation of integrals.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion On the distribution of points in a cube and the approximate evaluation of integrals

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.506652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 68d587ca-613b-4d30-bfc0-437c5c48c63e · outbound

This paper cites Pgd in thermal transient problems with a moving heat source: A sensitivity study on factors affecting accuracy and efficiency.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Pgd in thermal transient problems with a moving heat source: A sensitivity study on factors affecting accuracy and efficiency

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.499237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c0c1275-205a-4b8b-ba51-fc9ffe627d91 · outbound

This paper cites On the calibration of thermo-microstructural simulation models for laser powder bed fusion process: Integrating physics-informed neural networks with cellular automata.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion On the calibration of thermo-microstructural simulation models for laser powder bed fusion process: Integrating physics-informed neural networks with cellular automata

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.491726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 25d885c9-8214-4f82-a0a9-e4667c27f7c7 · outbound

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

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Wavelet neural operator for solving parametric partial dif- ferential equations in computational mechanics problems

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.484429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a5b760eb-b56a-4869-929d-cf5f3a66423d · outbound

This paper cites 3d temperature field prediction in direct energy deposition of metals using physics informed neural network.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion 3d temperature field prediction in direct energy deposition of metals using physics informed neural network

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.476615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a75af393-a3c1-45a3-9930-7fd12c362f9b · outbound

This paper cites Process planning for adaptive contour parallel tool- path in additive manufacturing with variable bead width.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Process planning for adaptive contour parallel tool- path in additive manufacturing with variable bead width

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.469025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4fc8827d-d6cc-482d-8056-e40784d7408b · outbound

This paper cites Fast and accurate reduced-order modeling of a moose-based additive manufacturing model with operator learning.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Fast and accurate reduced-order modeling of a moose-based additive manufacturing model with operator learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.461281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c1d0e7f7-ee44-499c-9a2a-038b6b9a08e8 · outbound

This paper cites Modeling and cooling rate control in laser additive manufacturing: 1-d pde formulation.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Modeling and cooling rate control in laser additive manufacturing: 1-d pde formulation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:24:43.453448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c591a04f-8a69-451b-9d02-2a084aeba963 · outbound

This paper cites an unresolved cited work.

Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:24:43.445719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 5eae5c86-b72a-4fa6-b561-f72709a23191 · inbound

Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards cites this paper.

Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:26:28.433536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.