Paper Citation Record · LEDGER

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

As of 17 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 2 inbound Pith citation observations for arXiv:2505.22391.

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

pith.paper-citation-record.v1
2505.22391 v2

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:12:41.827814Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T02:22:20.548346Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T03:56:21.918725Z

Reference resolution

67 of 67 outbound references displayed

verified exact3
verified fuzzy14
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External citation measurements

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Outbound references

Observation e1b6bef2-364c-4fff-8e92-ab86bd6b8ac5 · outbound

This paper cites {TensorFlow}: a system for {Large-Scale} machine learning.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation {TensorFlow}: a system for {Large-Scale} machine learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:35.371495Z

Source-reported events for the cited work

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Observation 71ddbab7-31ef-439b-bb15-60bbb5ce8605 · outbound

This paper cites Reverse-time diffusion equation models.Stochastic Processes and their Applications, 12(3):313–326, 1982.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Reverse-time diffusion equation models.Stochastic Processes and their Applications, 12(3):313–326, 1982

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:35.455015Z

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Observation 2c193732-a263-4564-a8f4-346e277c5237 · outbound

This paper cites Physics-informed diffusion models.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Physics-informed diffusion models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:35.522970Z

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Observation f863aa6e-d6c1-465b-91bb-b7bb83d256e1 · outbound

This paper cites D-Flow: Differentiating through Flows for Controlled Generation.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation D-Flow: Differentiating through Flows for Controlled Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:35.594552Z

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Observation 98e767a4-1908-48f0-9ec6-5fd9b18c5777 · outbound

This paper cites TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:35.653673Z

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Observation 37bd3110-5717-4be2-9a8d-64f80e0d43ec · outbound

This paper cites Find: Fine-tuning initial noise distribution with policy optimization for diffusion models.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Find: Fine-tuning initial noise distribution with policy optimization for diffusion models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:46.661526Z

Source-reported events for the cited work

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Observation 09e89c9d-4461-4f07-b14a-1ef1dc47bfe1 · outbound

This paper cites Masked Autoencoders Are Effective Tokenizers for Diffusion Models.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Masked Autoencoders Are Effective Tokenizers for Diffusion Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:35.779135Z

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Observation 59fd8f36-7fac-4365-8caf-914765fedfcf · outbound

This paper cites Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:46.443316Z

Source-reported events for the cited work

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Observation 7a9b407a-1614-4773-9a09-fcb87a64c858 · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:35.929262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26bdb444-7ffd-413d-bd3d-1762babf4a67 · outbound

This paper cites Oxford University Press, 2 edition, 1975.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Oxford University Press, 2 edition, 1975

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:46.185854Z

Source-reported events for the cited work

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Observation 0b4012c3-3aff-4c19-aeb9-745062f49ce9 · outbound

This paper cites Davidson.Turbulence: An Introduction for Scientists and Engineers.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Davidson.Turbulence: An Introduction for Scientists and Engineers

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:46.039019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be73822b-b929-4ecf-8485-c4a32b257457 · outbound

This paper cites The helmholtz machine.Neural computation, 7(5):889–904, 1995.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation The helmholtz machine.Neural computation, 7(5):889–904, 1995

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:45.865204Z

Source-reported events for the cited work

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Observation fb7e281d-d47a-4e51-8888-ab7efd89a6e5 · outbound

This paper cites Tweedie’s formula and selection bias.Journal of the American Statistical Association, 106(496):1602–1614, 2011.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Tweedie’s formula and selection bias.Journal of the American Statistical Association, 106(496):1602–1614, 2011

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:36.368901Z

Source-reported events for the cited work

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Observation a0038a42-a407-4994-951f-65249f1e72a1 · outbound

This paper cites Scaling rectified flow transform- ers for high-resolution image synthesis.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Scaling rectified flow transform- ers for high-resolution image synthesis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:36.461855Z

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Observation 8a5903fa-e12b-49b4-9747-0514ea99d61f · outbound

This paper cites Bounds on the Jensen Gap, and Implications for Mean-Concentrated Distributions.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Bounds on the Jensen Gap, and Implications for Mean-Concentrated Distributions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:36.581086Z

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Observation 1ea827d8-ed3d-4a51-b8ba-236c30dafb9f · outbound

This paper cites Initno: Boosting text-to-image diffusion models via initial noise optimization.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Initno: Boosting text-to-image diffusion models via initial noise optimization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:36.746889Z

Source-reported events for the cited work

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Observation 8a733580-33fd-48ae-aefb-8fa9b015bcae · outbound

This paper cites On the Feature Learning in Diffusion Models.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation On the Feature Learning in Diffusion Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:36.894577Z

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Observation e2c63e88-7523-4232-a2a7-0fd465bcf62a · outbound

This paper cites Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:37.036842Z

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Observation 441eb6d2-3caa-422f-8d85-8dc97e3e7e0e · outbound

This paper cites Learning physical models that can respect conservation laws.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Learning physical models that can respect conservation laws

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:37.122241Z

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Observation 33ec22f2-21cb-440e-bfe6-62e81adba256 · outbound

This paper cites Reducing the dimensionality of data with neural networks.science, 313(5786):504–507, 2006.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Reducing the dimensionality of data with neural networks.science, 313(5786):504–507, 2006

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:37.215907Z

Source-reported events for the cited work

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Observation 19ce4256-9d41-49af-baff-3d454cfa5c9b · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:37.342209Z

Source-reported events for the cited work

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Observation 5d39424b-4b17-49d3-a444-8f0c850e8ed5 · outbound

This paper cites Classifier-free diffusion guidance, 2022.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Classifier-free diffusion guidance, 2022

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:37.413222Z

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Observation 9ee40687-c8af-467b-a430-213abd89790f · outbound

This paper cites DiffusionPDE: Generative PDE-solving under partial observation.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation DiffusionPDE: Generative PDE-solving under partial observation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:45.504393Z

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Observation 4b992f39-90f8-4dab-b49d-bc3c7a6ae1aa · outbound

This paper cites Courier Corporation, 2003.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Courier Corporation, 2003

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:45.322859Z

Source-reported events for the cited work

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Observation a3b245a8-7548-4984-99f4-3b117b5adaed · outbound

This paper cites Incropera, David P.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Incropera, David P

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:45.175119Z

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Observation 5daeb710-bd74-48c2-889d-484459e4e44a · outbound

This paper cites John Wiley & Sons, 3 edition, 1998.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation John Wiley & Sons, 3 edition, 1998

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:45.011646Z

Source-reported events for the cited work

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Observation 15bb8451-ebca-4d56-a3cf-76e1cdc75ef1 · outbound

This paper cites Cocogen: Physically-consistent and conditioned score-based generative models for forward and inverse problems, 2024.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Cocogen: Physically-consistent and conditioned score-based generative models for forward and inverse problems, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:44.893711Z

Source-reported events for the cited work

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Observation 2afbec21-4ab7-4f05-a27e-5b526ccef6ec · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

Reference 28

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Observation e22b49cd-f9e3-4637-8e48-af0a58ce46db · outbound

This paper cites Functional Flow Matching.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Functional Flow Matching

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:38.119191Z

Source-reported events for the cited work

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Observation 46151f89-a454-4423-b5db-8f9ecf8a6111 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Adam: A Method for Stochastic Optimization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:38.223112Z

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Observation 408a33ce-d2fb-40e6-8f4b-ddfa1b16b0ab · outbound

This paper cites Auto-encoding variational bayes, 2013.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Auto-encoding variational bayes, 2013

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:38.330757Z

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Observation b61e60fb-88ae-4af6-9eaf-fa57f124bd60 · outbound

This paper cites SIAM, 2007.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation SIAM, 2007

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:38.464752Z

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Observation 27ddea3a-58db-403e-8a05-82ec65891c27 · outbound

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Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:12:44.645649Z

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Observation b5f0b3e1-44b9-422d-ae98-42ddafca8984 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Fourier Neural Operator for Parametric Partial Differential Equations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:38.627814Z

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Observation 5daa4a9f-17f9-44ef-95e5-f9adb4c31fa6 · outbound

This paper cites Physics-informed neural operator for learning partial differential equations.ACM/JMS Journal of Data Science, 1(3):1–27, 2024.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Physics-informed neural operator for learning partial differential equations.ACM/JMS Journal of Data Science, 1(3):1–27, 2024

Reference 35

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Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Unresolved cited work

Reference 36

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Observation 8901b130-8a37-4943-9d35-b26272b6a746 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.044369Z

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Observation 6df9deb0-eb0e-4b5d-a299-59e24d6ebf02 · outbound

This paper cites Instaflow: One step is enough for high-quality diffusion-based text-to-image generation.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Instaflow: One step is enough for high-quality diffusion-based text-to-image generation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.128905Z

Source-reported events for the cited work

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Observation 4d681c5b-e8f3-4c5d-b2e5-307c4a4c0ebc · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787, 2022.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787, 2022

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.219708Z

Source-reported events for the cited work

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Observation 202c078e-4259-4065-86ee-0b58f61bd4e4 · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.252347Z

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Observation dc0a1a75-09df-482c-b560-06bc911b7d8e · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.344943Z

Source-reported events for the cited work

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Observation 89d8bd48-6333-44bc-a5be-668206466ba2 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.433353Z

Source-reported events for the cited work

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Observation 29d64455-86b0-4cc1-859c-9784f6beaa92 · outbound

This paper cites The lottery ticket hypothesis in denoising: Towards semantic-driven initialization.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation The lottery ticket hypothesis in denoising: Towards semantic-driven initialization

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.511448Z

Source-reported events for the cited work

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Observation 9c8461e6-0736-4453-8bbe-a4229bc9698d · outbound

This paper cites Interacting particle solutions of fokker–planck equations through gradient–log–density estimation.Entropy, 22(8):802, 2020.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Interacting particle solutions of fokker–planck equations through gradient–log–density estimation.Entropy, 22(8):802, 2020

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.616773Z

Source-reported events for the cited work

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Observation ab032f56-20a6-4594-a245-9123eaa89c58 · outbound

This paper cites Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:12:42.981135Z

Source-reported events for the cited work

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Observation a9444f5e-7a02-446c-b2b2-a289ad7454f3 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Improved denoising diffusion probabilistic models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.837416Z

Source-reported events for the cited work

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Observation 2a28bdae-2e15-46f1-8be2-86d3ad1d7add · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:39.969844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e9d0336-bb7d-4610-b42b-09918e1a713e · outbound

This paper cites an unresolved cited work.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:40.053578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea7a4af9-406a-421e-9318-519f8158ee40 · outbound

This paper cites Stochastic backpropagation and approximate inference in deep generative models.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Stochastic backpropagation and approximate inference in deep generative models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:40.125201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f45a135f-4214-44a6-9d0c-0c3766da09e3 · outbound

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

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation High- resolution image synthesis with latent diffusion models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:40.236870Z

Source-reported events for the cited work

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Observation baee35d6-d403-40a5-a868-a73ac5edc8bb · outbound

This paper cites Guiding continuous operator learning through Physics-based boundary constraints.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Guiding continuous operator learning through Physics-based boundary constraints

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:12:42.647002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dba6b2ad-a7cc-418a-afd0-e3faf991d0a9 · outbound

This paper cites Parallel sampling of diffusion models.Advances in Neural Information Processing Systems, 36, 2024.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Parallel sampling of diffusion models.Advances in Neural Information Processing Systems, 36, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:44.200924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a69ea2d9-cfb8-45b0-b105-cf6d29cda1a3 · outbound

This paper cites A physics-informed diffusion model for high- fidelity flow field reconstruction.Journal of Computational Physics, 478:111972, 2023.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation A physics-informed diffusion model for high- fidelity flow field reconstruction.Journal of Computational Physics, 478:111972, 2023

Reference 54

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This paper cites Oxford university press, 1985.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Oxford university press, 1985

Reference 55

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Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Denoising diffusion implicit models

Reference 56

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This paper cites Consistency models, 2023.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Consistency models, 2023

Reference 57

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Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Score-based generative modeling through stochastic differential equations

Reference 58

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This paper cites Timoshenko and James N.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Timoshenko and James N

Reference 59

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This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 60

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This paper cites Noise re-sampling for high fidelity image generation, 2025.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Noise re-sampling for high fidelity image generation, 2025

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:43.862962Z

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This paper cites The Silent Assistant: NoiseQuery as Implicit Guidance for Goal-Driven Image Generation.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation The Silent Assistant: NoiseQuery as Implicit Guidance for Goal-Driven Image Generation

Reference 62

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This paper cites One-step diffusion with distribution matching distillation.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation One-step diffusion with distribution matching distillation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:41.301106Z

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This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 64

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Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Fast sampling of diffusion models via operator learning

Reference 65

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Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Fast ode-based sampling for diffusion models in around 5 steps

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:43.655283Z

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Observation 83434783-d063-49f0-8cff-4e850683f4f5 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation Golden Noise for Diffusion Models: A Learning Framework

Reference 67

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This paper cites The weighting λtrain is aligned with our setup across datasets.

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation The weighting λtrain is aligned with our setup across datasets

Reference 68

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Pith citing papers

Observation d40639f4-1329-49ea-8a5e-2c7a57cef4b2 · inbound

Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow cites this paper.

Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

Reference 77

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Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation cites this paper.

Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

Reference 52

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