{"id":6916,"date":"2025-09-17T07:02:31","date_gmt":"2025-09-17T07:02:31","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/09\/17\/2509-13189\/"},"modified":"2025-09-17T07:02:31","modified_gmt":"2025-09-17T07:02:31","slug":"2509-13189","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/09\/17\/2509-13189\/","title":{"rendered":"SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty"},"content":{"rendered":"<p>    SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2509.13189v1 Announce Type: new<br \/>\nAbstract: We present a direct inverse modeling method named SURGIN, a SURrogate-guided Generative INversion framework tailed for subsurface multiphase flow data assimilation. Unlike existing inversion methods that require adaptation for each new observational configuration, SURGIN features a zero-shot conditional generation capability, enabling real-time assimilation of unseen monitoring data without task-specific retraining. Specifically, SURGIN synergistically integrates a U-Net enhanced Fourier Neural Operator (U-FNO) surrogate with a score-based generative model (SGM), framing the conditional generation as a surrogate prediction-guidance process in a Bayesian perspective. Instead of directly learning the conditional generation of geological parameters, an unconditional SGM is first pretrained in a self-supervised manner to capture the geological prior, after which posterior sampling is performed by leveraging a differentiable U-FNO surrogate to enable efficient forward evaluations conditioned on unseen observations. Extensive numerical experiments demonstrate SURGIN&#8217;s capability to decently infer heterogeneous geological fields and predict spatiotemporal flow dynamics with quantified uncertainty across diverse measurement settings. By unifying generative learning with surrogate-guided Bayesian inference, SURGIN establishes a new paradigm for inverse modeling and uncertainty quantification in parametric functional spaces.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Zhao Feng, Bicheng Yan, Luanxiao Zhao, Xianda Shen, Renyu Zhao, Wenhao Wang, Fengshou Zhang<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2509.13189\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>SURGIN: SURrogate-guided Generative INversion for subsurface multiphase flow with quantified uncertainty arXiv:2509.13189v1 Announce Type: new Abstract: We present a direct inverse modeling method named SURGIN, a SURrogate-guided Generative INversion framework tailed for subsurface multiphase flow data assimilation. Unlike existing inversion methods that require adaptation for each new observational configuration, SURGIN features a zero-shot conditional generation [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,3810,3811,112],"tags":[252,3812,1350],"class_list":["post-6916","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-physics-flu-dyn","category-physics-geo-ph","category-stat-ml","tag-generative","tag-surgin","tag-surrogate"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6916"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=6916"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6916\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=6916"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=6916"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=6916"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}