Paper Citation Record · LEDGER

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders

As of 12 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.19320.

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

pith.paper-citation-record.v1
2505.19320 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:22:22.532973Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

28 of 28 outbound references displayed

verified exact1
verified fuzzy22
unresolved5
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e9302623-1600-471f-b076-6da0d6ec78c3 · outbound

This paper cites High-resolution image synthesis with la- tent diffusion models.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders High-resolution image synthesis with la- tent diffusion models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:26.382285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 596d9ed1-db5f-4e63-8506-17bde4b5c88b · outbound

This paper cites Generative adversarial nets.Advances in neural informa- tion processing systems, 27, 2014.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Generative adversarial nets.Advances in neural informa- tion processing systems, 27, 2014

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:26.215287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f4a65660-1fb2-42ea-a4de-a90c8fde4281 · outbound

This paper cites Auto-Encoding Variational Bayes.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Auto-Encoding Variational Bayes

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:20.810013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28fac075-d2d2-4d59-8499-7b767146512a · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.Journal of Machine Learning Research, 22(57):1–64, 2021.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Normalizing flows for probabilistic modeling and inference.Journal of Machine Learning Research, 22(57):1–64, 2021

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:20.869886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43ad1c8a-9ea5-4b9f-a57b-dc5020a06dfd · outbound

This paper cites Denoising diffusion probabilistic models.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Denoising diffusion probabilistic models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:20.923212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 763effa3-fe66-4d61-b206-207e690d1184 · outbound

This paper cites Time- series generative adversarial networks.Advances in neural infor- mation processing systems, 32, 2019.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Time- series generative adversarial networks.Advances in neural infor- mation processing systems, 32, 2019

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:26.017409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5b2b6f9c-b1d0-4686-92ed-cb76428491dd · outbound

This paper cites Vector quantized time series generation with a bidirectional prior model.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Vector quantized time series generation with a bidirectional prior model

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:25.871809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a3c5546-6581-427f-a84d-a8b46c6efe0b · outbound

This paper cites Generative time-series modeling with fourier flows.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Generative time-series modeling with fourier flows

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:25.645357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 124ec292-f3e2-4ba5-8607-716bb1ffa3d4 · outbound

This paper cites TSGBench: Time Series Generation Benchmark.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders TSGBench: Time Series Generation Benchmark

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:22:22.737865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 38b676f0-1aa6-4f45-845b-eb926bed6eb5 · outbound

This paper cites The Gaussian Process Prior VAE for Interpretable Latent Dynamics from Pixels.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders The Gaussian Process Prior VAE for Interpretable Latent Dynamics from Pixels

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:25.389972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a02c33f9-e9c2-4e14-8f43-d3bdd0fe5cd8 · outbound

This paper cites Scalable gaussian process variational autoencoders.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Scalable gaussian process variational autoencoders

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:25.260550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 106585d5-aa56-4f62-a5c9-7c066c2fe5bc · outbound

This paper cites Fully Bayesian Autoencoders with Latent Sparse Gaus- sian Processes.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Fully Bayesian Autoencoders with Latent Sparse Gaus- sian Processes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:25.110645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aba920f5-9a00-4077-ade6-3eb582642f26 · outbound

This paper cites Neural discrete rep- resentation learning.Advances in neural information processing systems, 30, 2017.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Neural discrete rep- resentation learning.Advances in neural information processing systems, 30, 2017

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:24.911304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7305ac77-83d0-40bb-92d9-17fbf9b5da36 · outbound

This paper cites Gaussian process prior variational autoencoders.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Gaussian process prior variational autoencoders

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:24.746249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7f974d83-969f-4249-90bf-8da905c4f703 · outbound

This paper cites The Gaussian process prior vae for interpretable latent dynamics from pixels.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders The Gaussian process prior vae for interpretable latent dynamics from pixels

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:24.535037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 29ce998a-7191-46d8-b7bd-dd2e8c66cb6c · outbound

This paper cites Physics-informed machine learning.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Physics-informed machine learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:21.778490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea66ae97-217e-44db-9023-ff857ed96c0d · outbound

This paper cites Bayesian calibration of computer models.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Bayesian calibration of computer models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:24.324892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4c2e24fb-53c4-47e2-b4ff-5ff1e3b87b68 · outbound

This paper cites Deep Gaussian processes for calibration of computer models (with discussion).

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Deep Gaussian processes for calibration of computer models (with discussion)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:24.173211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3fce4cb5-ae4c-47dc-97fb-213dd7803785 · outbound

This paper cites Bayesian calibration of imperfect computer models using physics-informed priors.Journal of Machine Learning Research, 24(108):1–39, 2023.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Bayesian calibration of imperfect computer models using physics-informed priors.Journal of Machine Learning Research, 24(108):1–39, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:24.064168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef1b7334-9217-4761-bc40-5e2eea97af67 · outbound

This paper cites Physics-Integrated Vari- ational Autoencoders for Robust and Interpretable Generative Modeling.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Physics-Integrated Vari- ational Autoencoders for Robust and Interpretable Generative Modeling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:23.929118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2bd6fe57-1cf0-4c61-906c-85bd8b676a71 · outbound

This paper cites Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differ- ential equations.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differ- ential equations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:23.749524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7829e23c-f8c8-4814-8a24-dc37cdb10d1e · outbound

This paper cites The rico dataset: a multivariate hvac indoors and outdoors time-series dataset.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders The rico dataset: a multivariate hvac indoors and outdoors time-series dataset

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:23.633566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 78cf80cb-ca0a-47e1-9fb3-74bee0906b70 · outbound

This paper cites TSGM: A flexible framework for generative modeling of synthetic time series.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders TSGM: A flexible framework for generative modeling of synthetic time series

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:23.462853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b84c9f0e-0ab3-487b-9788-14dc2f39f83a · outbound

This paper cites A kernel two-sample test.The Journal of Machine Learning Research, 13(1):723–773, 2012.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders A kernel two-sample test.The Journal of Machine Learning Research, 13(1):723–773, 2012

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:22.324374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6058c004-a14d-4fc3-af84-cd551008600d · outbound

This paper cites Sig-Wasserstein GANs for time series generation.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Sig-Wasserstein GANs for time series generation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:23.282306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8943205b-be48-46d9-b394-b1f3132edd07 · outbound

This paper cites GP-VAE: Deep Probabilistic Time Series Imputation.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders GP-VAE: Deep Probabilistic Time Series Imputation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:23.118603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a122184-52a7-4e3d-8ed6-207ff86e5a48 · outbound

This paper cites Markovian Gaussian process variational autoencoders.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Markovian Gaussian process variational autoencoders

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:23.028933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0054d157-fb23-4f31-82c5-4ccd7b1cca2c · outbound

This paper cites Fully Bayesian autoencoders with latent sparse Gaus- sian processes.

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders Fully Bayesian autoencoders with latent sparse Gaus- sian processes

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:22:22.893953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.