Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:03:39.752186Z
Paper Citation Record · LEDGER
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis
As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2411.09237.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:01:11.503446Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T19:01:11.641675Z
Reference resolution
38 of 38 outbound references displayed
- verified exact0
- verified fuzzy29
- unresolved9
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 49b971df-efb8-47fe-9b60-960b0bb83a21 · outbound
This paper cites an unresolved cited work.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T21:03:41.058295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation eb8f800e-6c65-44cc-8690-c7c2b70e8ed5 · outbound
This paper cites an unresolved cited work.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Unresolved cited work
Reference 2
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 58ef9aca-c05f-48c8-9e79-2dd9fd06a514 · outbound
This paper cites an unresolved cited work.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Unresolved cited work
Reference 3
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 682bcb35-203e-4fbe-8724-065b1dae273a · outbound
This paper cites Invariant manifold based reduced-order observer design for nonlinear systems.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Invariant manifold based reduced-order observer design for nonlinear systems
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.832806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ac7aefa0-e5bd-4f94-b9e0-69dd31df6423 · outbound
This paper cites Linearization by output injection and nonlinear observers.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Linearization by output injection and nonlinear observers
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.817946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 86a4a83a-cb92-4e3d-bb1d-5f4f5a1f29b4 · outbound
This paper cites Rajamani.
Reference 6
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2f31b430-c8da-43df-b10c-cb1dde3e09cc · outbound
This paper cites Non-asymptotic neural network-based state and disturbance estimation for a class of nonlinear systems using modulating functions.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Non-asymptotic neural network-based state and disturbance estimation for a class of nonlinear systems using modulating functions
Reference 7
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3e8bf2d6-c3f5-4a30-b066-d8e3e9503e5b · outbound
This paper cites Kazantzis and C.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Kazantzis and C
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.772132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6fcc96de-c867-4a18-948c-1c189d3f5fdb · outbound
This paper cites Anderson and J.B.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Anderson and J.B
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.757400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6df5348c-a47c-4634-92d0-3eae680c1673 · outbound
This paper cites Lohmiller and J.-J.E.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Lohmiller and J.-J.E
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.741968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 99eca38a-3412-45b6-98ba-8fa20a6c4b05 · outbound
This paper cites Lohmiller and Slotine.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Lohmiller and Slotine
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.726729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 902dc944-3e40-44d2-ae6e-bcc7af4197e5 · outbound
This paper cites Nonlinear dynamical control systems.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Nonlinear dynamical control systems
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.710411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 983430fa-65d5-4089-adce-df88b1ae16af · outbound
This paper cites an unresolved cited work.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Unresolved cited work
Reference 13
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 52e649d5-7e15-469f-9f80-3b6398fd7a11 · outbound
This paper cites an unresolved cited work.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T21:03:40.593442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e4fe3f4b-33fb-41d1-b96d-cc02feaea8cd · outbound
This paper cites Contracting nonlinear observers: Convex opti- mization and learning from data.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Contracting nonlinear observers: Convex opti- mization and learning from data
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.575791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c0879a78-2c03-4498-aa08-9db0668d073c · outbound
This paper cites Lohmiller and J.-J.E.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Lohmiller and J.-J.E
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.559017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation bd06c74d-3be1-4fdb-bdcc-e825d20baf93 · outbound
This paper cites Lohmiller and J.-J.E.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Lohmiller and J.-J.E
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.544152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6e5b71f7-c7c4-4f6a-ae54-7de3c54ec45e · outbound
This paper cites Convergence of nonlinear observers on Rn with a riemannian metric (part i).
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Convergence of nonlinear observers on Rn with a riemannian metric (part i)
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.528920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 970ccd3c-35c0-4da9-8c00-16b5124fd39d · outbound
This paper cites Observer de- sign for stochastic nonlinear systems via contraction-based incremental stability.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Observer de- sign for stochastic nonlinear systems via contraction-based incremental stability
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.514365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation bfe3a21d-b22d-47b3-9ad7-26bfecdabbda · outbound
This paper cites Review on contraction analysis and computation of contraction metrics.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Review on contraction analysis and computation of contraction metrics
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.498884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ff40fc18-94b3-4034-9994-e4f2d0ac8d55 · outbound
This paper cites Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Reference 21
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20653b01-0762-49f2-a888-1a47e35e8746 · outbound
This paper cites Parameter es- timation and modeling of nonlinear dynamical systems based on runge–kutta physics-informed neural network.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Parameter es- timation and modeling of nonlinear dynamical systems based on runge–kutta physics-informed neural network
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.398500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 50e6f250-6ae5-4ae4-9917-73018ea0fe57 · outbound
This paper cites Nonlinear discrete-time observers with physics-informed neural networks.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Nonlinear discrete-time observers with physics-informed neural networks
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.358751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1ba94f4a-9d23-4d5b-b972-82df66d68504 · outbound
This paper cites Learning-based design of luenberger observers for autonomous nonlinear systems.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Learning-based design of luenberger observers for autonomous nonlinear systems
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.343926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 26df1a71-0b9e-4d2e-adbd-5e44f79977eb · outbound
This paper cites Deep learning-based luenberger observer design for discrete-time nonlinear systems.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Deep learning-based luenberger observer design for discrete-time nonlinear systems
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.329256Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4f3be4d2-1bed-4837-84ec-894834f20986 · outbound
This paper cites Deep-learning based design of cascade observers for discrete-time nonlinear systems with output delay.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Deep-learning based design of cascade observers for discrete-time nonlinear systems with output delay
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.168700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c3fb47d2-e3c8-40b9-8a08-6090e8c9b204 · outbound
This paper cites Deep-learning based kkl chain observer for discrete-time nonlinear systems with time-varying output delay.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Deep-learning based kkl chain observer for discrete-time nonlinear systems with time-varying output delay
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.153363Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ae28559a-9e49-48fe-9277-1289ea124a29 · outbound
This paper cites Physics-informed neural nets for control of dynamical systems.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Physics-informed neural nets for control of dynamical systems
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.136700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2f1822b6-963d-4c61-bca3-a9f8c6f973dc · outbound
This paper cites Physics-informed neural networks with skip connections for modeling and control of gas-lifted oil wells.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Physics-informed neural networks with skip connections for modeling and control of gas-lifted oil wells
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.120257Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7a471e73-eff7-4fa7-bee7-d852ffa9c628 · outbound
This paper cites Stability of Motion.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Stability of Motion
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:40.104811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation cf388dc6-40be-4019-baf5-5647521fc13b · outbound
This paper cites Observer design for continuous-time dynamical systems.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Observer design for continuous-time dynamical systems
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:39.966946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7be75895-6100-4cc1-b77d-6ae84c54bb8d · outbound
This paper cites Positive definite matrices and sylvester’s criterion.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Positive definite matrices and sylvester’s criterion
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:39.951660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 70c0d864-8efe-4956-99c9-ddc08429c6a1 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Adam: A Method for Stochastic Optimization
Reference 33
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd941ca3-325a-4090-bc70-d125a46d69c9 · outbound
This paper cites A limited memory algorithm for bound constrained optimization.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis A limited memory algorithm for bound constrained optimization
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:39.936226Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c937c584-d684-4e49-ba1a-3f7b48255e27 · outbound
This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Challenges in Training PINNs: A Loss Landscape Perspective
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T21:03:39.703770Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fa268114-49d9-440c-ad9a-6cc20d5ed580 · outbound
This paper cites Physics-informed neural networks for solving forward and inverse flow problems via the boltzmann-bgk formulation.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Physics-informed neural networks for solving forward and inverse flow problems via the boltzmann-bgk formulation
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T21:03:39.708483Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c4b74f9-0c8d-4f66-be4f-9b6ce159edf4 · outbound
This paper cites A mixed pressure-velocity formulation to model flow in heterogeneous porous media with physics-informed neural networks.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis A mixed pressure-velocity formulation to model flow in heterogeneous porous media with physics-informed neural networks
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:39.912382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fc35ce35-6531-4f68-b143-ad7b1472da0d · outbound
This paper cites Improved training of physics-informed neural networks with model ensembles.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis Improved training of physics-informed neural networks with model ensembles
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T21:03:39.896269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
Observation cb9d49f3-18b0-4124-8581-0ad5d7e826d4 · inbound
Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control cites this paper.
Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:01:11.650731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.