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Paper Citation Record · LEDGER

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series

As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2508.11528.

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

pith.paper-citation-record.v1
2508.11528 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:37:40.632230Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation dba48e7b-50f6-4dec-a3ad-bb8914d4b3ed · outbound

This paper cites Janot, M.G., Brunot, M.: Data set and reference models of emps.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Janot, M.G., Brunot, M.: Data set and reference models of emps

Reference 1

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Observation 62421ddc-4f76-4b69-9ab7-ca6dbbbd1ba7 · outbound

This paper cites Physics-Informed Diffusion Models.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Physics-Informed Diffusion Models

Reference 2

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 3

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This paper cites Journal of the American statistical Association 112(518), 859–877 (2017).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Journal of the American statistical Association 112(518), 859–877 (2017)

Reference 4

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Observation 921d7759-ac82-4aef-abcc-83401c188f35 · outbound

This paper cites ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Reference 5

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This paper cites Constrained Synthesis with Projected Diffusion Models.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Constrained Synthesis with Projected Diffusion Models

Reference 6

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 7

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Observation af4d32a8-01ae-43ba-bcdd-6c697605ac9b · outbound

This paper cites TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Reference 8

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 9

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This paper cites In: Asian Conference on Machine Learning.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: Asian Conference on Machine Learning

Reference 10

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This paper cites In: 2021 IEEE 17th International Conference on Automation Science and Engi- neering (CASE).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: 2021 IEEE 17th International Conference on Automation Science and Engi- neering (CASE)

Reference 11

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Observation ca77a2f0-5372-407c-8218-f31e06a6c9f1 · outbound

This paper cites Advances in neural information processing systems33, 6840–6851 (2020).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Advances in neural information processing systems33, 6840–6851 (2020)

Reference 12

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This paper cites Scholarpedia1(10), 1563 (2006).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Scholarpedia1(10), 1563 (2006)

Reference 13

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This paper cites In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 14

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This paper cites CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems

Reference 15

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This paper cites IEEE Access 7, 143608–143619 (2019).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series IEEE Access 7, 143608–143619 (2019)

Reference 16

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This paper cites Advances in neural information processing systems34, 21696–21707 (2021).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Advances in neural information processing systems34, 21696–21707 (2021)

Reference 17

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 18

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 19

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: Interna- tional conference on artificial neural networks

Reference 20

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 21

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This paper cites Machine Learning for Computational Science and Engineering1(1), 1–23 (2025).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Machine Learning for Computational Science and Engineering1(1), 1–23 (2025)

Reference 22

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This paper cites In: 2020 IEEE Power & Energy Society General Meeting (PESGM).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: 2020 IEEE Power & Energy Society General Meeting (PESGM)

Reference 23

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This paper cites Engineering Applications of Arti- ficial Intelligence 131, 107696 (2024).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Engineering Applications of Arti- ficial Intelligence 131, 107696 (2024)

Reference 24

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This paper cites MIT Press (2022), probml.ai.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series MIT Press (2022), probml.ai

Reference 25

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series MIT Press (2023), http://probml.github.io/book2

Reference 26

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This paper cites In: International Conference on Machine Learning.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: International Conference on Machine Learning

Reference 27

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This paper cites Sensors 20(13), 3738 (2020).

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Sensors 20(13), 3738 (2020)

Reference 28

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Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW)

Reference 29

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This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 30

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Observation e5e1bfe0-2fe2-4519-a927-61b21664b169 · outbound

This paper cites Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations

Reference 31

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Pith citing papers

Observation aadad655-aa67-49a1-9415-866568fb7f75 · inbound

Integrating Fourier Neural Operator with Diffusion Model for Autoregressive Predictions of Three-dimensional Turbulence cites this paper.

Integrating Fourier Neural Operator with Diffusion Model for Autoregressive Predictions of Three-dimensional Turbulence Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series

Reference 83

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