Paper Citation Record · LEDGER

Physics-informed sensor coverage through structure preserving machine learning

As of 7 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 2 inbound Pith citation observations for arXiv:2509.10363.

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

pith.paper-citation-record.v1
2509.10363 v1

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:58:21.109252Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:22:12.534960Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:46:32.360685Z

Reference resolution

73 of 73 outbound references displayed

verified exact0
verified fuzzy0
unresolved70
parse uncertain0
malformed identifier3
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54394fcf-759d-4c50-8501-8f5377748209 · outbound

This paper cites Data-driven whitney forms for structure-preserving control volume analysis.

Physics-informed sensor coverage through structure preserving machine learning Data-driven whitney forms for structure-preserving control volume analysis

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.270486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89c65a93-5f4e-4a95-b588-56f01eba2ad7 · outbound

This paper cites Riemannian lp center of mass: existence, uniqueness, and convexity.

Physics-informed sensor coverage through structure preserving machine learning Riemannian lp center of mass: existence, uniqueness, and convexity

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.307656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d92e71d-0f95-4965-82c1-09d5b4847361 · outbound

This paper cites Model-based solution techniques for the source localization problem.

Physics-informed sensor coverage through structure preserving machine learning Model-based solution techniques for the source localization problem

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.350960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 218b94c7-19d3-48bb-9068-f020fee25a92 · outbound

This paper cites Finite element exterior calculus.

Physics-informed sensor coverage through structure preserving machine learning Finite element exterior calculus

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.433626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29b218b3-540b-4a65-a90b-be1afef75031 · outbound

This paper cites Finite element exterior calculus, 25 Fig.

Physics-informed sensor coverage through structure preserving machine learning Finite element exterior calculus, 25 Fig

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.493267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc77511e-576b-45eb-bb14-e8267e487a34 · outbound

This paper cites Solving inverse problems using data-driven models.

Physics-informed sensor coverage through structure preserving machine learning Solving inverse problems using data-driven models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.545139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5a6b347-c688-4317-9f75-43189f97b4c4 · outbound

This paper cites Iterative methods for approximate solution of inverse problems , volume 577.

Physics-informed sensor coverage through structure preserving machine learning Iterative methods for approximate solution of inverse problems , volume 577

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.653901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a416feb1-8cfa-4098-9c93-0def291491cf · outbound

This paper cites Inverse source problems in transport equations.

Physics-informed sensor coverage through structure preserving machine learning Inverse source problems in transport equations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.742493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5580ac2d-7b95-4b23-b21e-70f271f5c025 · outbound

This paper cites Clifford Neural Layers for PDE Modeling.

Physics-informed sensor coverage through structure preserving machine learning Clifford Neural Layers for PDE Modeling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.825172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 053c55eb-2e39-4a98-9b17-ea8be89f8868 · outbound

This paper cites Physics-informed neural networks (pinns) for fluid mechanics: A review.

Physics-informed sensor coverage through structure preserving machine learning Physics-informed neural networks (pinns) for fluid mechanics: A review

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.910102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1894bc72-dbe0-4434-8dc8-1c6c79b0b635 · outbound

This paper cites Nonlinear least squares for inverse problems: theoretical foundations and step- by-step guide for applications.

Physics-informed sensor coverage through structure preserving machine learning Nonlinear least squares for inverse problems: theoretical foundations and step- by-step guide for applications

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:14.992217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 778e7427-59ee-4ab9-bc3e-6f0f76703c76 · outbound

This paper cites Neural symplectic form: Learning hamiltonian equations on general coordinate systems.

Physics-informed sensor coverage through structure preserving machine learning Neural symplectic form: Learning hamiltonian equations on general coordinate systems

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.074024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73004122-1c5e-43cc-8436-e966caf6d392 · outbound

This paper cites Group equivariant convolutional networks.

Physics-informed sensor coverage through structure preserving machine learning Group equivariant convolutional networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.129203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d86dc06b-de97-4fb1-9b81-39c206adf0b4 · outbound

This paper cites Coverage control for mobile sensing networks.

Physics-informed sensor coverage through structure preserving machine learning Coverage control for mobile sensing networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.198642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66d0111d-bc54-4f7f-bc1b-9fb4990e3fe1 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Physics-informed sensor coverage through structure preserving machine learning Sinkhorn distances: Lightspeed computation of optimal transport

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.247352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84ed712a-9d7a-4814-838c-5d975105942b · outbound

This paper cites Deep learning architectures for nonlinear operator functions and nonlinear inverse problems.

Physics-informed sensor coverage through structure preserving machine learning Deep learning architectures for nonlinear operator functions and nonlinear inverse problems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.328472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8ebb1d5-b347-482a-a893-0058909067f2 · outbound

This paper cites Discrete Exterior Calculus.

Physics-informed sensor coverage through structure preserving machine learning Discrete Exterior Calculus

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.395145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bf6a415-05ed-4f15-9c82-49a2a8e04b38 · outbound

This paper cites Bacterium-inspired robots for environmental monitoring.

Physics-informed sensor coverage through structure preserving machine learning Bacterium-inspired robots for environmental monitoring

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.477754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da347469-b619-46b9-bac9-6754134320d6 · outbound

This paper cites Centroidal voronoi tessellations: Applications and algorithms.

Physics-informed sensor coverage through structure preserving machine learning Centroidal voronoi tessellations: Applications and algorithms

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.549101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eb88bc7-ed21-43c3-ba60-31819c19a0d4 · outbound

This paper cites Regularization of inverse problems.

Physics-informed sensor coverage through structure preserving machine learning Regularization of inverse problems

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.614188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e42d1431-c9c7-470d-8672-a5239d9c5696 · outbound

This paper cites Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities.

Physics-informed sensor coverage through structure preserving machine learning Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.705889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9f807dc-d1bc-4f61-963a-e7dee9eaf737 · outbound

This paper cites Hamiltonian neural networks.

Physics-informed sensor coverage through structure preserving machine learning Hamiltonian neural networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.784033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a349ecf3-a599-43b0-97a5-7b3e3c54a3d6 · outbound

This paper cites scikit-fem: A python package for finite element assembly.

Physics-informed sensor coverage through structure preserving machine learning scikit-fem: A python package for finite element assembly

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.843951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98fecafc-93c2-4f7e-9656-f959aa441831 · outbound

This paper cites Multi-agent search for source localization in a turbulent medium.

Physics-informed sensor coverage through structure preserving machine learning Multi-agent search for source localization in a turbulent medium

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.892075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca197c97-3f8a-4b2e-ae90-00363f6e6c37 · outbound

This paper cites Information theoretic source seeking strate- gies for multiagent plume tracking in turbulent fields.

Physics-informed sensor coverage through structure preserving machine learning Information theoretic source seeking strate- gies for multiagent plume tracking in turbulent fields

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:15.971481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6412135b-3d0c-4d25-9229-606e89c76876 · outbound

This paper cites Structure-preserving neural networks.

Physics-informed sensor coverage through structure preserving machine learning Structure-preserving neural networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.051647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5d4586f-53f3-4c2a-83d2-a101c708aca6 · outbound

This paper cites Hycom surface velocity fields for the gulf of mexico and the florida straits at 1km resolution for january 2014 and july 2014, 2019.

Physics-informed sensor coverage through structure preserving machine learning Hycom surface velocity fields for the gulf of mexico and the florida straits at 1km resolution for january 2014 and july 2014, 2019

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.111934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93b9b0e2-ea8a-42da-9d7c-ff4a3f582fdf · outbound

This paper cites Inverse source problems.

Physics-informed sensor coverage through structure preserving machine learning Inverse source problems

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.172242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4a609c1-0486-4ec4-8166-f57685c34c58 · outbound

This paper cites Physics- informed neural networks for inverse problems in supersonic flows.

Physics-informed sensor coverage through structure preserving machine learning Physics- informed neural networks for inverse problems in supersonic flows

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.232195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9132b2b7-9416-4b58-93bc-aff69bf5f78c · outbound

This paper cites A Structure-Preserving Domain Decomposition Method for Data-Driven Modeling.

Physics-informed sensor coverage through structure preserving machine learning A Structure-Preserving Domain Decomposition Method for Data-Driven Modeling

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.284845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66442371-6cd3-4e30-ab95-7066bd5f3371 · outbound

This paper cites Deep learning methods for inverse problems.

Physics-informed sensor coverage through structure preserving machine learning Deep learning methods for inverse problems

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.368966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f13bc5d-82c5-4074-a24c-63f2d23f7217 · outbound

This paper cites Physics-informed machine learning.

Physics-informed sensor coverage through structure preserving machine learning Physics-informed machine learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.423385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b5315d8-3aea-4cbd-9c92-509422f33cce · outbound

This paper cites Generalized coverage control for time- varying density functions.

Physics-informed sensor coverage through structure preserving machine learning Generalized coverage control for time- varying density functions

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.474801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c7edf8c-0a29-40ff-a3fa-595e3a5232c8 · outbound

This paper cites Model-based active source identification in complex environments.

Physics-informed sensor coverage through structure preserving machine learning Model-based active source identification in complex environments

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.564149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f0b2f8-f943-43b8-865a-e8bc9111b2de · outbound

This paper cites Computing geodesic paths on manifolds.

Physics-informed sensor coverage through structure preserving machine learning Computing geodesic paths on manifolds

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.625758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a6b4a3b-87bd-482e-a1fd-602ba41b843d · outbound

This paper cites Structure-Preserving Digital Twins via Conditional Neural Whitney Forms.

Physics-informed sensor coverage through structure preserving machine learning Structure-Preserving Digital Twins via Conditional Neural Whitney Forms

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.738129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4fd5906-981b-4d2e-a047-b25cc1562aa1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Physics-informed sensor coverage through structure preserving machine learning Adam: A Method for Stochastic Optimization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:16.868478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79ada893-f11f-4726-85be-cdcfe1f221c3 · outbound

This paper cites Machine learning for groundwater pollution source identification and monitoring network optimization.

Physics-informed sensor coverage through structure preserving machine learning Machine learning for groundwater pollution source identification and monitoring network optimization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.004584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07b7da44-f0ef-4f15-b547-61c1b30726aa · outbound

This paper cites Multirobot control using time- varying density functions.

Physics-informed sensor coverage through structure preserving machine learning Multirobot control using time- varying density functions

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.097845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f638ce91-7c47-43b0-8258-b0b251d27c27 · outbound

This paper cites Controlled coverage using time-varying density functions.

Physics-informed sensor coverage through structure preserving machine learning Controlled coverage using time-varying density functions

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.159861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f75ad07-349e-4391-aaf5-4e2530992fee · outbound

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

Physics-informed sensor coverage through structure preserving machine learning Fourier Neural Operator for Parametric Partial Differential Equations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.227666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0e67d60-fe2c-42e5-b9ff-bdb3baad2f8d · outbound

This paper cites Explainable ai: A review of machine learning interpretability methods.

Physics-informed sensor coverage through structure preserving machine learning Explainable ai: A review of machine learning interpretability methods

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.324396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c13ef207-6b97-4e6c-97ea-5f413719d9e5 · outbound

This paper cites Least squares quantization in pcm.

Physics-informed sensor coverage through structure preserving machine learning Least squares quantization in pcm

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.424495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4f85125-b28f-4133-9560-9264277a9223 · outbound

This paper cites Whitney forms and their extensions.

Physics-informed sensor coverage through structure preserving machine learning Whitney forms and their extensions

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.568481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dce1e4e7-2279-4605-87e2-fc7c425876e5 · outbound

This paper cites Learn- ing nonlinear operators via deeponet based on the universal approximation theorem of operators.

Physics-informed sensor coverage through structure preserving machine learning Learn- ing nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.663390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07592aee-968a-4f17-ae9a-c8b6b9b119d4 · outbound

This paper cites Distributed environmental 27 modeling and adaptive sampling for multi-robot sensor coverage.

Physics-informed sensor coverage through structure preserving machine learning Distributed environmental 27 modeling and adaptive sampling for multi-robot sensor coverage

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.766861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 341af6ef-c220-4355-ba7f-7fd739d582bc · outbound

This paper cites Olfaction-based mobile robot navi- gation.

Physics-informed sensor coverage through structure preserving machine learning Olfaction-based mobile robot navi- gation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:17.864635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1027e069-2bb7-4d82-b87e-0b4ac1d5726f · outbound

This paper cites Source localization by spatially distributed electronic noses for advection and diffusion.

Physics-informed sensor coverage through structure preserving machine learning Source localization by spatially distributed electronic noses for advection and diffusion

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.091191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2ea1551-8078-4b50-aca6-62316baf02c4 · outbound

This paper cites Estimates on the generalization error of physics- informed neural networks for approximating a class of inverse problems for pdes.

Physics-informed sensor coverage through structure preserving machine learning Estimates on the generalization error of physics- informed neural networks for approximating a class of inverse problems for pdes

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.124786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99ebc821-c6b1-4398-bbc6-e03eb577d4cf · outbound

This paper cites The discrete geodesic problem.

Physics-informed sensor coverage through structure preserving machine learning The discrete geodesic problem

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.228386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f56f5369-b64f-4bc7-b3ce-56c9af6d8f78 · outbound

This paper cites Neural Inverse Operators for Solving PDE Inverse Problems.

Physics-informed sensor coverage through structure preserving machine learning Neural Inverse Operators for Solving PDE Inverse Problems

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.298470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcdb2e0f-baa1-4782-a7f0-66d872fd2a39 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Physics-informed sensor coverage through structure preserving machine learning PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.392284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 248edbdf-b060-42a1-a1e3-6327f29410d5 · outbound

This paper cites Thermodynamically consistent physics-informed neural networks for hyperbolic systems.

Physics-informed sensor coverage through structure preserving machine learning Thermodynamically consistent physics-informed neural networks for hyperbolic systems

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.547998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e8a41f8-936e-46e3-b2ba-0ae8ce6a36f7 · 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 sensor coverage through structure preserving machine learning Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.646803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b3688e-41e0-40b4-82bd-e07bb2724d08 · outbound

This paper cites A machine learning approach to identifying point source locations in photoacoustic data.

Physics-informed sensor coverage through structure preserving machine learning A machine learning approach to identifying point source locations in photoacoustic data

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.778780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1c9c4fb-4394-4e5e-bebb-786b221eca37 · outbound

This paper cites Corbijn van Willenswaard.

Physics-informed sensor coverage through structure preserving machine learning Corbijn van Willenswaard

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:18.905393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7255cab3-7ec9-4d74-a368-a45f586bebf1 · outbound

This paper cites On the definition and importance of interpretability in scientific machine learning.

Physics-informed sensor coverage through structure preserving machine learning On the definition and importance of interpretability in scientific machine learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.068457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf44fab2-15af-48c9-9b05-4a9f7dfbd82b · outbound

This paper cites A compar- ison of reactive robot chemotaxis algorithms.

Physics-informed sensor coverage through structure preserving machine learning A compar- ison of reactive robot chemotaxis algorithms

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.224341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 324153c0-efa2-4fb9-9099-ee84ccfcc513 · outbound

This paper cites Decentralized minimum-energy coverage control for time-varying density functions.

Physics-informed sensor coverage through structure preserving machine learning Decentralized minimum-energy coverage control for time-varying density functions

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.357413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df18df2-13d1-4514-9cf8-fa337fa6bdbe · outbound

This paper cites A distributed formation-based odor source localization algorithm-design, implementation, and wind tun- nel evaluation.

Physics-informed sensor coverage through structure preserving machine learning A distributed formation-based odor source localization algorithm-design, implementation, and wind tun- nel evaluation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.511117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 253f1eee-1132-4c69-b98d-92b150735c40 · outbound

This paper cites Inverse problems: a bayesian perspective.

Physics-informed sensor coverage through structure preserving machine learning Inverse problems: a bayesian perspective

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.622989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57752cd5-f92f-46aa-b304-ddbfb03cf540 · outbound

This paper cites Inverse problem theory and methods for model parameter estimation.

Physics-informed sensor coverage through structure preserving machine learning Inverse problem theory and methods for model parameter estimation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.804385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28088578-24de-4ccd-9615-6f69b7fa1ed7 · outbound

This paper cites Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs.

Physics-informed sensor coverage through structure preserving machine learning Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.864182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45111259-70d4-4851-8680-a04acea692aa · outbound

This paper cites Contaminant source identifica- tion using semi-supervised machine learning.

Physics-informed sensor coverage through structure preserving machine learning Contaminant source identifica- tion using semi-supervised machine learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:19.979334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65774551-a15a-4cd4-a021-3b9a7d2ed9c6 · outbound

This paper cites Physics-informed neural network algorithm for solving forward and inverse problems of variable-order space-fractional advection–diffusion equations.

Physics-informed sensor coverage through structure preserving machine learning Physics-informed neural network algorithm for solving forward and inverse problems of variable-order space-fractional advection–diffusion equations

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:20.122055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8559b661-dbed-4b13-8cd9-79bfbac41a8e · outbound

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

Physics-informed sensor coverage through structure preserving machine learning Understanding and mitigating gradient flow pathologies in physics-informed neural networks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:20.282404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9095ae02-8615-4826-b05a-bc358ce61f50 · outbound

This paper cites Bioinspired algorithm for autonomous sensor- driven guidance in turbulent chemical plumes.

Physics-informed sensor coverage through structure preserving machine learning Bioinspired algorithm for autonomous sensor- driven guidance in turbulent chemical plumes

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:20.399118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa9528ca-a89b-4853-8064-765ae79bab1b · outbound

This paper cites Multiple source detection and localization in advection-diffusion processes using wireless sensor networks.

Physics-informed sensor coverage through structure preserving machine learning Multiple source detection and localization in advection-diffusion processes using wireless sensor networks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:20.518132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36b78367-0fc4-4222-bf41-ea1cb34bfdb5 · outbound

This paper cites Foundational research gaps and future directions for digital twins.

Physics-informed sensor coverage through structure preserving machine learning Foundational research gaps and future directions for digital twins

Reference 69

Resolution: malformed identifier; no resolver link, observed 2026-08-04T17:58:20.618849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1db675d-eeea-42fe-b2e1-83af3e3efdaa · outbound

This paper cites Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.

Physics-informed sensor coverage through structure preserving machine learning Gradient-enhanced physics- informed neural networks for forward and inverse pde problems

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:20.776736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb5753e-da41-49c7-9f5b-1d3e49cad9b5 · outbound

This paper cites Distributed robotics approach to chemical plume tracing.

Physics-informed sensor coverage through structure preserving machine learning Distributed robotics approach to chemical plume tracing

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-04T17:58:20.855174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12d9990c-97b8-4ae3-b1c7-81213ef3d1a1 · outbound

This paper cites We provide a proof for Theorem 5.4 Proof.

Physics-informed sensor coverage through structure preserving machine learning We provide a proof for Theorem 5.4 Proof

Reference 72

Resolution: malformed identifier; no resolver link, observed 2026-08-04T17:58:20.969532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e75ab68-b5ba-4027-bc73-5a828b386f02 · outbound

This paper cites Using the simple bounds arctan( θ) ≤ θ and tan(θ) ≤ 2θ, it is straightforward to show (10.17) θ∗ 1 = arctan r′ 1 − r′ tan(θ1) ≤ 2r′ 1 − r′ θ1.

Physics-informed sensor coverage through structure preserving machine learning Using the simple bounds arctan( θ) ≤ θ and tan(θ) ≤ 2θ, it is straightforward to show (10.17) θ∗ 1 = arctan r′ 1 − r′ tan(θ1) ≤ 2r′ 1 − r′ θ1

Reference 73

Resolution: malformed identifier; no resolver link, observed 2026-08-04T17:58:21.109252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 41071298-b392-449a-bf63-47be56aa180c · inbound

Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs cites this paper.

Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs Physics-informed sensor coverage through structure preserving machine learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T05:22:12.534960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6956b51e-a6f3-48da-94d9-724a4f114256 · inbound

Neural Navigation Functions for Zero-Shot Generalizable Motion Planning cites this paper.

Neural Navigation Functions for Zero-Shot Generalizable Motion Planning Physics-informed sensor coverage through structure preserving machine learning

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:46:32.362594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.