Paper Citation Record · LEDGER

Universal Physics Simulation: A Foundational Diffusion Approach

As of 14 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.09733.

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

pith.paper-citation-record.v1
2507.09733 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:53:34.694695Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

33 of 33 outbound references displayed

verified exact2
verified fuzzy23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6dc9fc34-77ba-4476-86b6-73fc98f95737 · outbound

This paper cites Scalable Diffusion Models with Transformers.

Universal Physics Simulation: A Foundational Diffusion Approach Scalable Diffusion Models with Transformers

Reference 1

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Source-reported events for the cited work

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Observation d726b8b0-475f-45f9-a64c-ae842dfd83e9 · outbound

This paper cites Physics-informed neural net- works: A deep learning framework for solving forward and inverse problems involving nonlin- ear partial differential equations.

Universal Physics Simulation: A Foundational Diffusion Approach Physics-informed neural net- works: A deep learning framework for solving forward and inverse problems involving nonlin- ear partial differential equations

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:40.146978Z

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Observation 188b4f8d-bcab-4575-b1c3-7a804e3105b6 · outbound

This paper cites U-Sketch: An efficient ap- proach for sketch to image diffusion models.

Universal Physics Simulation: A Foundational Diffusion Approach U-Sketch: An efficient ap- proach for sketch to image diffusion models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:39.875635Z

Source-reported events for the cited work

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Observation b625560a-5b92-4610-baad-278e9568b86a · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Universal Physics Simulation: A Foundational Diffusion Approach An image is worth 16x16 words: Transformers for image recognition at scale

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:39.580011Z

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Observation 4de9b88d-9b70-4f74-9a12-79582179d192 · outbound

This paper cites Physics-informed ma- chine learning.

Universal Physics Simulation: A Foundational Diffusion Approach Physics-informed ma- chine learning

Reference 5

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Source-reported events for the cited work

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Observation 0c236902-7486-415e-8b64-14f35b6b2311 · outbound

This paper cites A physics-informed diffusion model for high-fidelity flow field reconstruc- tion.

Universal Physics Simulation: A Foundational Diffusion Approach A physics-informed diffusion model for high-fidelity flow field reconstruc- tion

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:39.198394Z

Source-reported events for the cited work

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Observation 4d7838e3-ec8e-4dd1-9e1c-865fbcc974d7 · outbound

This paper cites Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models.

Universal Physics Simulation: A Foundational Diffusion Approach Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:38.900341Z

Source-reported events for the cited work

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Observation 88f8894a-815e-4374-a1a5-d82d8075493b · outbound

This paper cites GeoDiff: A geometric diffusion model for molecular conformation generation.

Universal Physics Simulation: A Foundational Diffusion Approach GeoDiff: A geometric diffusion model for molecular conformation generation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:38.654936Z

Source-reported events for the cited work

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Observation e7435b92-425d-4dce-b020-e799c2c43384 · outbound

This paper cites Crystal diffusion variational au- toencoder for periodic material generation.

Universal Physics Simulation: A Foundational Diffusion Approach Crystal diffusion variational au- toencoder for periodic material generation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:38.452784Z

Source-reported events for the cited work

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Observation 23ef6676-6f7a-4557-9471-19f4a04ce8c0 · outbound

This paper cites Conditional diffusion-based microstructure reconstruction.

Universal Physics Simulation: A Foundational Diffusion Approach Conditional diffusion-based microstructure reconstruction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:38.286205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d1a2164e-1422-4044-8e20-477ba76ac65e · outbound

This paper cites Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN.

Universal Physics Simulation: A Foundational Diffusion Approach Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:53:32.113317Z

Source-reported events for the cited work

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Observation 16616b93-0fd1-4140-af69-ef68a6c9656a · outbound

This paper cites Constrained synthe- sis with projected diffusion models.

Universal Physics Simulation: A Foundational Diffusion Approach Constrained synthe- sis with projected diffusion models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:38.055544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6d7c5d1-a9df-4ea4-84cd-e163255c13e6 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Universal Physics Simulation: A Foundational Diffusion Approach Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:53:32.294261Z

Source-reported events for the cited work

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Observation fa4dd790-5e98-49e4-b8d6-8af7ac50f961 · outbound

This paper cites Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery.

Universal Physics Simulation: A Foundational Diffusion Approach Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:53:34.995801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f5705af7-bf04-4010-bcff-de54df214cc0 · outbound

This paper cites Universal Physics Transform- ers: A Framework For Efficiently Scaling Neural Operators.

Universal Physics Simulation: A Foundational Diffusion Approach Universal Physics Transform- ers: A Framework For Efficiently Scaling Neural Operators

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:37.892931Z

Source-reported events for the cited work

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Observation d5948fa3-16a9-4181-ae55-42683f5ba89c · outbound

This paper cites Denoising diffusion probabilistic models.

Universal Physics Simulation: A Foundational Diffusion Approach Denoising diffusion probabilistic models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:37.742301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e905aac1-5880-438b-960c-264a181b3209 · outbound

This paper cites ControlAR: Controllable Image Generation with Autoregressive Models.

Universal Physics Simulation: A Foundational Diffusion Approach ControlAR: Controllable Image Generation with Autoregressive Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T17:53:32.673269Z

Source-reported events for the cited work

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Observation e2601dd6-e32d-44b8-8c90-4d885fa4dd76 · outbound

This paper cites An optimal control perspec- tive on diffusion-based generative modeling.

Universal Physics Simulation: A Foundational Diffusion Approach An optimal control perspec- tive on diffusion-based generative modeling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:37.641191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0dcf35b4-c1f2-4a0c-baeb-db040454f299 · outbound

This paper cites PhysDiff: Physics-Guided Human Motion Diffusion Model.

Universal Physics Simulation: A Foundational Diffusion Approach PhysDiff: Physics-Guided Human Motion Diffusion Model

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:53:34.883680Z

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Observation 76766441-2340-45c5-bbd7-60243e2fd152 · outbound

This paper cites Attention is all you need.

Universal Physics Simulation: A Foundational Diffusion Approach Attention is all you need

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:37.441509Z

Source-reported events for the cited work

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Observation 750dd950-2ec9-413b-ac8f-ec7c506b4304 · outbound

This paper cites Layer Normalization.

Universal Physics Simulation: A Foundational Diffusion Approach Layer Normalization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T17:53:33.016547Z

Source-reported events for the cited work

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Observation c89e460f-c855-4bfd-b341-64f420e90f41 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Universal Physics Simulation: A Foundational Diffusion Approach High-resolution image syn- thesis with latent diffusion models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:37.276595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 648d68ad-5fe0-4556-afa8-a71e5a37a9d3 · outbound

This paper cites U-Net: Convolutional networks for biomedical image segmentation.

Universal Physics Simulation: A Foundational Diffusion Approach U-Net: Convolutional networks for biomedical image segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:37.058408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 80007767-0ecf-4df0-b16b-bd8185c8b994 · outbound

This paper cites an unresolved cited work.

Universal Physics Simulation: A Foundational Diffusion Approach Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:53:36.805266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 59633a33-6038-4cf0-af14-3a14965c8a00 · outbound

This paper cites Numerical solution of initial bound- ary value problems involving Maxwell’s equa- tions in isotropic media.

Universal Physics Simulation: A Foundational Diffusion Approach Numerical solution of initial bound- ary value problems involving Maxwell’s equa- tions in isotropic media

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:36.575665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ed3c6c58-66a1-4af7-9228-1308a3eaa8d2 · outbound

This paper cites Denoising diffusion implicit mod- els.

Universal Physics Simulation: A Foundational Diffusion Approach Denoising diffusion implicit mod- els

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:36.348490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54728cb3-ea33-4c25-a1c5-07fb3edf7c04 · outbound

This paper cites Diffusion models beat GANs on image synthesis.

Universal Physics Simulation: A Foundational Diffusion Approach Diffusion models beat GANs on image synthesis

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:36.083452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a26c433c-3fda-41c4-b85b-b386bf635f86 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Universal Physics Simulation: A Foundational Diffusion Approach Progressive Distillation for Fast Sampling of Diffusion Models

Reference 28

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Source-reported events for the cited work

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Observation 223ecba2-6e47-415e-ac77-e90662dd77ca · outbound

This paper cites Fast sampling of diffusion mod- els with exponential integrator.

Universal Physics Simulation: A Foundational Diffusion Approach Fast sampling of diffusion mod- els with exponential integrator

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:35.852455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 44e4a4ac-c3e2-4b44-84f7-5ce64b21ed3d · outbound

This paper cites A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization.

Universal Physics Simulation: A Foundational Diffusion Approach A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T17:53:34.488689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48929aad-d307-4ab4-8c3b-f9b5a79263ac · outbound

This paper cites Bayesian deep convolutional en- coder–decoder networks for surrogate model- ing and uncertainty quantification.

Universal Physics Simulation: A Foundational Diffusion Approach Bayesian deep convolutional en- coder–decoder networks for surrogate model- ing and uncertainty quantification

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:35.666945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d005ce6c-de60-4548-9836-ef2a40f3f79b · outbound

This paper cites Learning transferable visual models from natural language supervision.

Universal Physics Simulation: A Foundational Diffusion Approach Learning transferable visual models from natural language supervision

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:35.413948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9e2dcdc8-ed14-4a24-9c03-b2141b9221ba · outbound

This paper cites DiffuseBot: Breeding soft robots with physics-augmented generative dif- fusion models.

Universal Physics Simulation: A Foundational Diffusion Approach DiffuseBot: Breeding soft robots with physics-augmented generative dif- fusion models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:53:35.254951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.