{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:25:22Z","timestamp":1787689522870,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2027,4,20]],"date-time":"2027-04-20T00:00:00Z","timestamp":1808179200000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Artificial Intelligence Research Resource &#x28;NAIRR&#x29;","award":["240280"],"award-info":[{"award-number":["240280"]}]},{"name":"National Artificial Intelligence Research Resource &#x28;NAIRR&#x29;","award":["240443"],"award-info":[{"award-number":["240443"]}]},{"name":"National Science Foundation","award":["2106859"],"award-info":[{"award-number":["2106859"]}]},{"name":"National Science Foundation","award":["2211557"],"award-info":[{"award-number":["2211557"]}]},{"name":"National Science Foundation","award":["2119643"],"award-info":[{"award-number":["2119643"]}]},{"name":"National Science Foundation","award":["2200274"],"award-info":[{"award-number":["2200274"]}]},{"name":"National Science Foundation","award":["2303037"],"award-info":[{"award-number":["2303037"]}]},{"name":"National Science Foundation","award":["2312501"],"award-info":[{"award-number":["2312501"]}]},{"name":"National Science Foundation","award":["2531008"],"award-info":[{"award-number":["2531008"]}]},{"name":"National Institutes of Health","award":["U54HG012517"],"award-info":[{"award-number":["U54HG012517"]}]},{"name":"National Institutes of Health","award":["U24DK097771"],"award-info":[{"award-number":["U24DK097771"]}]},{"name":"National Institutes of Health","award":["U54OD036472"],"award-info":[{"award-number":["U54OD036472"]}]},{"name":"National Institutes of Health","award":["OT2OD038003"],"award-info":[{"award-number":["OT2OD038003"]}]},{"name":"National Institutes of Health","award":["R01HL175135"],"award-info":[{"award-number":["R01HL175135"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3780314","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"714-723","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Guided Diffusion Model for Accelerating Computational Fluid Dynamics"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5098-7900","authenticated-orcid":false,"given":"Ruoyan","family":"Li","sequence":"first","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1263-9270","authenticated-orcid":false,"given":"Zijie","family":"Huang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5714-0149","authenticated-orcid":false,"given":"Haixin","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7083-6423","authenticated-orcid":false,"given":"Guancheng","family":"Wan","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-6843","authenticated-orcid":false,"given":"Yizhou","family":"Sun","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8180-2886","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Haddad","author":"Akansu Ali N.","year":"1992","unstructured":"Ali N. Akansu and Richard A. Haddad. 1992. Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets. Academic Press, San Diego, CA."},{"key":"e_1_3_2_2_2_1","volume-title":"Physics-Informed Diffusion Models. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=tpYeermigp","author":"Bastek Jan-Hendrik","year":"2025","unstructured":"Jan-Hendrik Bastek, WaiChing Sun, and Dennis Kochmann. 2025. Physics-Informed Diffusion Models. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=tpYeermigp"},{"key":"e_1_3_2_2_3_1","volume-title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models. arXiv:2108.02938 [cs.CV] https:\/\/arxiv.org\/abs\/2108.02938","author":"Choi Jooyoung","year":"2021","unstructured":"Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon. 2021. ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models. arXiv:2108.02938 [cs.CV] https:\/\/arxiv.org\/abs\/2108.02938"},{"key":"e_1_3_2_2_4_1","volume-title":"Diffusion Posterior Sampling for General Noisy Inverse Problems. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=OnD9zGAGT0k","author":"Chung Hyungjin","year":"2023","unstructured":"Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, and Jong Chul Ye. 2023. Diffusion Posterior Sampling for General Noisy Inverse Problems. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=OnD9zGAGT0k"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01448839"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970104"},{"key":"e_1_3_2_2_7_1","volume-title":"Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction. In The Second Learning on Graphs Conference. https:\/\/openreview.net\/forum?id=695IYJh1Ba","author":"Fu Cong","year":"2023","unstructured":"Cong Fu, Jacob Helwig, and Shuiwang Ji. 2023. Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction. In The Second Learning on Graphs Conference. https:\/\/openreview.net\/forum?id=695IYJh1Ba"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1017\/jfm.2019.238"},{"key":"e_1_3_2_2_9_1","volume-title":"Woods","author":"Gonzalez Rafael C.","year":"2002","unstructured":"Rafael C. Gonzalez and Richard E. Woods. 2002. Digital Image Processing (2nd ed.). Prentice Hall."},{"key":"e_1_3_2_2_10_1","volume-title":"Denoising Diffusion Probabilistic Models. arXiv preprint arxiv:2006.11239","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising Diffusion Probabilistic Models. arXiv preprint arxiv:2006.11239 (2020)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Jiahe Huang Guandao Yang Zichen Wang and Jeong Joon Park. 2024. DiffusionPDE: Generative PDE-Solving Under Partial Observation. arXiv:2406.17763 [cs.LG] https:\/\/arxiv.org\/abs\/2406.17763","DOI":"10.52202\/079017-4140"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599362"},{"key":"e_1_3_2_2_13_1","first-page":"1","volume-title":"MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (2020","author":"Jiang Chiyu Max","year":"2020","unstructured":"Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa A. Mustafa, Hamdi A. Tchelepi, Philip S. Marcus, Prabhat, and Anima Anandkumar. 2020. MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (2020), 1-15. https:\/\/api.semanticscholar.org\/CorpusID:218487006"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Bahjat Kawar Michael Elad Stefano Ermon and Jiaming Song. 2022. Denoising Diffusion Restoration Models. In Advances in Neural Information Processing Systems.","DOI":"10.52202\/068431-1714"},{"key":"e_1_3_2_2_15_1","volume-title":"Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR)","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR) (2015). https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4053671"},{"key":"e_1_3_2_2_17_1","volume-title":"Guy Van den Broeck, and Zhe Zeng.","author":"Li Ruoyan","year":"2025","unstructured":"Ruoyan Li, Dipti Ranjan Sahu, Guy Van den Broeck, and Zhe Zeng. 2025a. Deep Generative Models with Hard Linear Equality Constraints. arXiv:2502.05416 [cs.LG] https:\/\/arxiv.org\/abs\/2502.05416"},{"key":"e_1_3_2_2_18_1","volume-title":"Flow Field Reconstruction with Sensor Placement Policy Learning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=1esFEjMUBS","author":"Li Ruoyan","year":"2025","unstructured":"Ruoyan Li, Guancheng Wan, Zijie Huang, Zixiao Liu, Haixin Wang, Xiao Luo, Wei Wang, and Yizhou Sun. 2025b. Flow Field Reconstruction with Sensor Placement Policy Learning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=1esFEjMUBS"},{"key":"e_1_3_2_2_19_1","volume-title":"Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895","author":"Li Zongyi","year":"2020","unstructured":"Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M Stuart, and Anima Anandkumar. 2020. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895 (2020)."},{"key":"e_1_3_2_2_20_1","unstructured":"Zongyi Li Hongkai Zheng Nikola Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli and Anima Anandkumar. 2023. Physics-Informed Neural Operator for Learning Partial Differential Equations. arXiv:2111.03794 [cs.LG] https:\/\/arxiv.org\/abs\/2111.03794"},{"key":"e_1_3_2_2_21_1","volume-title":"DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. arXiv preprint arXiv:2206.00927","author":"Lu Cheng","year":"2022","unstructured":"Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. 2022. DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. arXiv preprint arXiv:2206.00927 (2022)."},{"key":"e_1_3_2_2_22_1","volume-title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. arXiv preprint arXiv:1910.03193","author":"Lu Lu","year":"2019","unstructured":"Lu Lu, Pengzhan Jin, and George Em Karniadakis. 2019. DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. arXiv preprint arXiv:1910.03193 (2019)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Andreas Lugmayr Martin Danelljan Andres Romero Fisher Yu Radu Timofte and Luc Van Gool. 2022. RePaint: Inpainting using Denoising Diffusion Probabilistic Models. arXiv:2201.09865 [cs.CV] https:\/\/arxiv.org\/abs\/2201.09865","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"e_1_3_2_2_24_1","volume-title":"Continuous Markov processes and stochastic equations. Rendiconti del Circolo Matematico di Palermo","author":"Maruyama Gisiro","year":"1955","unstructured":"Gisiro Maruyama. 1955. Continuous Markov processes and stochastic equations. Rendiconti del Circolo Matematico di Palermo, Vol. 4 (1955), 48-90."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0022112084001750"},{"key":"e_1_3_2_2_26_1","volume-title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. In International Conference on Learning Representations.","author":"Meng Chenlin","year":"2022","unstructured":"Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. 2022. SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/sapm1972513253"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Aniruddhe Pradhan and Karthik Duraisamy. 2021. Variational Multi-scale Super-resolution : A data-driven approach for reconstruction and predictive modeling of unresolved physics. arXiv:2101.09839 [physics.comp-ph] https:\/\/arxiv.org\/abs\/2101.09839","DOI":"10.2139\/ssrn.3991780"},{"key":"e_1_3_2_2_29_1","volume-title":"Pi-fusion: Physics-informed diffusion model for learning fluid dynamics. arXiv:2406.03711 [physics.flu-dyn] https:\/\/arxiv.org\/abs\/2406.03711","author":"Qiu Jing","year":"2024","unstructured":"Jing Qiu, Jiancheng Huang, Xiangdong Zhang, Zeng Lin, Minglei Pan, Zengding Liu, and Fen Miao. 2024. Pi-fusion: Physics-informed diffusion model for learning fluid dynamics. arXiv:2406.03711 [physics.flu-dyn] https:\/\/arxiv.org\/abs\/2406.03711"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2023.112438"},{"key":"e_1_3_2_2_31_1","volume-title":"Score-based Data Assimilation. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=VUvLSnMZdX","author":"Rozet Fran\u00e7ois","year":"2023","unstructured":"Fran\u00e7ois Rozet and Gilles Louppe. 2023. Score-based Data Assimilation. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=VUvLSnMZdX"},{"key":"e_1_3_2_2_32_1","volume-title":"Image super-resolution via iterative refinement. arXiv:2104.07636","author":"Saharia Chitwan","year":"2021","unstructured":"Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. 2021. Image super-resolution via iterative refinement. arXiv:2104.07636 (2021)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"e_1_3_2_2_34_1","volume-title":"Sagar Srinivas Sakhinana, and Venkataramana Runkana","author":"Sarkar Rajat Kumar","year":"2023","unstructured":"Rajat Kumar Sarkar, Ritam Majumdar, Vishal Jadhav, Sagar Srinivas Sakhinana, and Venkataramana Runkana. 2023. Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations. arXiv:2311.09740 [physics.flu-dyn] https:\/\/arxiv.org\/abs\/2311.09740"},{"key":"e_1_3_2_2_35_1","unstructured":"Siming Shan Pengkai Wang Song Chen Jiaxu Liu Chao Xu and Shengze Cai. 2024. PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction. arXiv:2404.08412 [physics.flu-dyn] https:\/\/arxiv.org\/abs\/2404.08412"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Dule Shu Zijie Li and Amir Barati Farimani. 2023. A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction. J. Comput. Phys. (2023) 111972.","DOI":"10.1016\/j.jcp.2023.111972"},{"key":"e_1_3_2_2_37_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=PxTIG12RRHS","author":"Song Yang","year":"2021","unstructured":"Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021. Score-Based Generative Modeling through Stochastic Differential Equations. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=PxTIG12RRHS"},{"key":"e_1_3_2_2_38_1","unstructured":"Haixin Wang Yadi Cao Zijie Huang Yuxuan Liu Peiyan Hu Xiao Luo Zezheng Song Wanjia Zhao Jilin Liu Jinan Sun et al. 2024. Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey. arXiv preprint arXiv:2408.12171 (2024)."},{"key":"e_1_3_2_2_39_1","unstructured":"Haixin Wang Ruoyan Li Fred Xu Fang Sun Kaiqiao Han Zijie Huang Guancheng Wan Ching Chang Xiao Luo Wei Wang and Yizhou Sun. 2025a. FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation. arXiv:2505.20349 [physics.flu-dyn] https:\/\/arxiv.org\/abs\/2505.20349"},{"key":"e_1_3_2_2_40_1","volume-title":"MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation. In Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=D1gs8QT74m","author":"Wang Qi","year":"2025","unstructured":"Qi Wang, Yuan Mi, Wang Haoyun, Yi Zhang, Ruizhi Chengze, Hongsheng Liu, Ji-Rong Wen, and Hao Sun. 2025b. MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation. In Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=D1gs8QT74m"},{"key":"e_1_3_2_2_41_1","volume-title":"Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model. The Eleventh International Conference on Learning Representations","author":"Wang Yinhuai","year":"2023","unstructured":"Yinhuai Wang, Jiwen Yu, and Jian Zhang. 2023. Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model. The Eleventh International Conference on Learning Representations (2023)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_3_2_2_43_1","volume-title":"Flow Matching for Scalable Simulation-Based Inference. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=D2cS6SoYlP","author":"Wildberger Jonas Bernhard","year":"2023","unstructured":"Jonas Bernhard Wildberger, Maximilian Dax, Simon Buchholz, Stephen R Green, Jakob H. Macke, and Bernhard Sch\u00f6lkopf. 2023. Flow Matching for Scalable Simulation-Based Inference. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=D2cS6SoYlP"},{"key":"e_1_3_2_2_44_1","unstructured":"Gefan Yang and Stefan Sommer. 2023. A Denoising Diffusion Model for Fluid Field Prediction. arXiv:2301.11661 [cs.LG] https:\/\/arxiv.org\/abs\/2301.11661"},{"key":"e_1_3_2_2_45_1","unstructured":"Ling Yang and Shenda Hong. 2022. Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion."},{"key":"e_1_3_2_2_46_1","volume-title":"Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks. In International Conference on Learning Representations.","author":"Yu Sihyun","year":"2022","unstructured":"Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin. 2022. Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_47_1","unstructured":"Zongsheng Yue Jianyi Wang and Chen Change Loy. 2023. ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting. arXiv:2307.12348 [cs.CV] https:\/\/arxiv.org\/abs\/2307.12348"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.3390\/jmse11071440"},{"key":"e_1_3_2_2_50_1","volume-title":"The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Nb3a8aUGfj","author":"Zhou Anthony","year":"2025","unstructured":"Anthony Zhou, Zijie Li, Michael Schneier, John R Buchanan Jr, and Amir Barati Farimani. 2025. Text2PDE: Latent Diffusion Models for Accessible Physics Simulation. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Nb3a8aUGfj"},{"key":"e_1_3_2_2_51_1","volume-title":"Denoising Diffusion Models for Plug-and-Play Image Restoration. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (NTIRE).","author":"Zhu Yuanzhi","year":"2023","unstructured":"Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, and Luc Van Gool. 2023. Denoising Diffusion Models for Plug-and-Play Image Restoration. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (NTIRE)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780314","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780314","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:15:17Z","timestamp":1787688917000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3780314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":51,"alternative-id":["10.1145\/3770854.3780314","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3780314","relation":{},"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"2026-04-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}