Paper Citation Record · LEDGER

Physics-informed sensor coverage through structure preserving machine learning

As of 22 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

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Pith citing papers itemized under the disclosed page cap.

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

Reference resolution

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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

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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

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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

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This paper cites Finite element exterior calculus.

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

Reference 4

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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

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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

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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

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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

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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

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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

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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

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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

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This paper cites Group equivariant convolutional networks.

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

Reference 13

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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

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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

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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

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This paper cites Discrete Exterior Calculus.

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

Reference 17

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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

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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

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This paper cites Regularization of inverse problems.

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

Reference 20

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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

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This paper cites Hamiltonian neural networks.

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

Reference 22

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

Reference 23

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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

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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

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This paper cites Structure-preserving neural networks.

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

Reference 26

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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

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This paper cites Inverse source problems.

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

Reference 28

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

Reference 29

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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

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

Reference 31

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

Reference 32

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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

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

Reference 34

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This paper cites Computing geodesic paths on manifolds.

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

Reference 35

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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

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

Reference 37

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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

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

Reference 39

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

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

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

Reference 42

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

Reference 43

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

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

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

Reference 46

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

Reference 47

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

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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

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

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

Reference 51

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

Reference 52

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

Reference 53

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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

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

Reference 55

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

Reference 56

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

Reference 57

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

Reference 58

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

Reference 59

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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

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

Reference 61

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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