Paper Citation Record · LEDGER

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control

As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2509.23960.

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

pith.paper-citation-record.v1
2509.23960 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:42:48.403356Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy0
unresolved33
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External citation measurements

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Outbound references

Observation f0885749-406d-4e1c-8036-ed9c16bc3011 · outbound

This paper cites Predictive control of aerial swarms in cluttered environments,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Predictive control of aerial swarms in cluttered environments,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:44.916179Z

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Observation f84fa97b-864e-422c-8919-c7f59a444e64 · outbound

This paper cites Multi-agent reinforcement learning in intelligent transportation systems: A comprehensive survey,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Multi-agent reinforcement learning in intelligent transportation systems: A comprehensive survey,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.037524Z

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Observation 893349e5-bcdc-45c5-8df3-4b2394c6a224 · outbound

This paper cites Distributed optimization in multi-agent robotics for industry 4.0 warehouses,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Distributed optimization in multi-agent robotics for industry 4.0 warehouses,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.097559Z

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Observation f3eb8cca-aa31-452f-9d7b-72ccf0aab64f · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Multi-agent actor-critic for mixed cooperative-competitive environments,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.253925Z

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Observation 99950b37-ac7c-43c8-b664-bd713379e777 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control The surprising effectiveness of ppo in cooperative multi-agent games,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.356082Z

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Observation 13c7768a-d9d2-418e-b5b4-17d0cee4deec · outbound

This paper cites Scalable multi-agent reinforcement learning through intelligent information aggregation,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Scalable multi-agent reinforcement learning through intelligent information aggregation,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.438746Z

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Observation 12ccff54-f6ce-4f29-b0c0-117027ea100f · outbound

This paper cites Altman,Constrained Markov Decision Processes, ser.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Altman,Constrained Markov Decision Processes, ser

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.507185Z

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Observation 1348f7eb-f649-413c-85c9-cfe6fe5b309f · outbound

This paper cites Safe multi-agent reinforcement learning for multi-robot control,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Safe multi-agent reinforcement learning for multi-robot control,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.640750Z

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Observation 059e9cc0-2f78-4a73-9956-f9f0d64f6ca2 · outbound

This paper cites Control barrier function based quadratic programs for safety critical systems,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Control barrier function based quadratic programs for safety critical systems,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.720316Z

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Observation 94cd7c74-962c-46a0-bb9a-c3e8f190f1e2 · outbound

This paper cites On safety and liveness filtering using hamilton–jacobi reachability analysis,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control On safety and liveness filtering using hamilton–jacobi reachability analysis,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.776321Z

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Observation ef717c6f-4512-46d3-ac9d-270063fd339d · outbound

This paper cites Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.859254Z

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Observation 5467cb65-4055-4655-a875-4cb422b66c8e · outbound

This paper cites The safety filter: A unified view of safety-critical control in autonomous systems,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control The safety filter: A unified view of safety-critical control in autonomous systems,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.918875Z

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Observation 1bb7a488-3c8e-4332-ba19-e5b68e53f0ac · outbound

This paper cites Resolving conflicting constraints in multi-agent reinforcement learning with layered safety,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Resolving conflicting constraints in multi-agent reinforcement learning with layered safety,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.977734Z

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Observation d35b2790-89a9-4e45-a73f-520e77c2c3d3 · outbound

This paper cites Learning a formally verified control barrier function in stochastic environment,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Learning a formally verified control barrier function in stochastic environment,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.068932Z

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Observation 51261a34-f756-46a1-bb32-333233719e49 · outbound

This paper cites Model predictive control: Theory and practice—a survey,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Model predictive control: Theory and practice—a survey,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.147945Z

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Observation 4443f300-79f9-4ae8-ad11-59fd981860fe · outbound

This paper cites Gr ¨une, J.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Gr ¨une, J

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.302983Z

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Observation 19b843f8-f289-48b0-babf-b989fd2220ad · outbound

This paper cites Information-theoretic model predictive control: Theory and applica- tions to autonomous driving,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Information-theoretic model predictive control: Theory and applica- tions to autonomous driving,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.407550Z

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Observation da9020f0-2ba7-4fcc-bf0b-98b78df7f5e9 · outbound

This paper cites Multi-agent path integral control for interaction-aware motion planning in urban canals,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Multi-agent path integral control for interaction-aware motion planning in urban canals,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.467092Z

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Observation fc25e335-79ef-49b6-9c1f-0ab09b369295 · outbound

This paper cites Multi-agent path integral control for interaction-aware motion planning in urban canals,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Multi-agent path integral control for interaction-aware motion planning in urban canals,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.533052Z

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Observation fac7e8f6-671b-4511-ab28-d7e83731397b · outbound

This paper cites Semi-Supervised Safe Visuomotor Policy Synthesis using Barrier Certificates.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Semi-Supervised Safe Visuomotor Policy Synthesis using Barrier Certificates

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.591864Z

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Observation afe7c7b6-2772-4bf5-8f7a-c06c5f1922d0 · outbound

This paper cites CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.647382Z

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Observation 5dd1bf48-b40c-4a9a-9709-f34de865d101 · outbound

This paper cites Optimal control with state-space constraint i,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Optimal control with state-space constraint i,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.734796Z

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Observation dc849ac4-6044-4493-b3a2-35b3629999c8 · outbound

This paper cites A general hamilton- jacobi framework for non-linear state-constrained control problems,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control A general hamilton- jacobi framework for non-linear state-constrained control problems,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.997050Z

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Observation 3e4d6936-4e43-4bbd-990c-63c5e92da93e · outbound

This paper cites A physics- informed machine learning framework for safe and optimal control of autonomous systems,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control A physics- informed machine learning framework for safe and optimal control of autonomous systems,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.119135Z

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Observation 72d3f81a-c25a-44cb-90af-0ffab3cc9c06 · outbound

This paper cites Boyd and L.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Boyd and L

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.250759Z

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Observation 5549ee73-5312-45f2-b4a0-d8a1ece7af64 · outbound

This paper cites A toolbox of level set methods,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control A toolbox of level set methods,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.414374Z

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Observation 3152bb6b-5bc3-47e7-939d-e237df69c718 · outbound

This paper cites hj reachability: Hamilton-Jacobi reachability analysis in JAX,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control hj reachability: Hamilton-Jacobi reachability analysis in JAX,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.584301Z

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Observation a974ec87-de01-40a6-b08c-4d41fc87706d · outbound

This paper cites Deepreach: A deep learning approach to high-dimensional reachability,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Deepreach: A deep learning approach to high-dimensional reachability,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.689106Z

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Observation 122fa0c3-b256-4a21-af11-258632636f82 · outbound

This paper cites Exact Imposition of Safety Boundary Conditions in Neural Reachable Tubes.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Exact Imposition of Safety Boundary Conditions in Neural Reachable Tubes

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.811130Z

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Observation 68344309-820c-43e4-b3f8-5b85906cded9 · outbound

This paper cites A time-dependent hamilton-jacobi formulation of reachable sets for continuous dynamic games,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control A time-dependent hamilton-jacobi formulation of reachable sets for continuous dynamic games,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.924429Z

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Observation 41c134e9-37ea-41c7-9395-0d26811b7797 · outbound

This paper cites On reachability and minimum cost optimal control,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control On reachability and minimum cost optimal control,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.064774Z

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Observation aef10e65-332c-41f6-af43-2ed49cfc4176 · outbound

This paper cites Solving multi- agent safe optimal control with distributed epigraph form MARL,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Solving multi- agent safe optimal control with distributed epigraph form MARL,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.202939Z

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Observation cb76c3af-d65a-43c5-af3f-6e64f9509a04 · outbound

This paper cites Verification of neural reachable tubes via scenario optimization and conformal prediction,.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Verification of neural reachable tubes via scenario optimization and conformal prediction,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.403356Z

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Observation 3de0d36d-1382-4746-ac15-2b668c5fddff · outbound

This paper cites Available: https://doi.org/10.1137/0324032.

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control Available: https://doi.org/10.1137/0324032

Reference 1986

Resolution: verified exact; doi, observed 2026-08-04T14:43:28.526742Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.