{"id":11146,"date":"2025-05-26T09:49:10","date_gmt":"2025-05-26T00:49:10","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=11146"},"modified":"2025-06-09T09:19:02","modified_gmt":"2025-06-09T00:19:02","slug":"machine-learning-methods-trained-on-simple-models-can-predict-critical-transitions-in-complex-natural-systems-shingo-gibo","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/machine-learning-methods-trained-on-simple-models-can-predict-critical-transitions-in-complex-natural-systems-shingo-gibo\/","title":{"rendered":"Machine learning methods trained on simple models can predict critical transitions in complex natural systems – Shingo Gibo"},"content":{"rendered":"

In this talk, we discuss the paper “Machine learning methods trained on simple models can predict critical transitions in complex natural systems” by\u00a0 Smita Deb, Sahil Sidheekh, Christopher F. Clements, Narayanan C. Krishnan, and Partha S. Dutta, in Royal Society Open Science, (2022).<\/p>\n

Abstract:\u00a0<\/strong><\/p>\n

Forecasting\u00a0sudden\u00a0changes\u00a0in\u00a0complex\u00a0systems\u00a0is\u00a0a\u00a0critical\u00a0but\u00a0challenging\u00a0task,\u00a0with\u00a0previously\u00a0developed\u00a0methods\u00a0varying\u00a0widely\u00a0in\u00a0their\u00a0reliability.\u00a0Here\u00a0we\u00a0develop\u00a0a\u00a0novel\u00a0detection\u00a0method,\u00a0using\u00a0simple\u00a0theoretical\u00a0models\u00a0to\u00a0train\u00a0a\u00a0deep\u00a0neural\u00a0network\u00a0to\u00a0detect\u00a0critical\u00a0transitions\u2014the\u00a0Early\u00a0Warning\u00a0Signal\u00a0Network\u00a0(EWSNet).\u00a0We\u00a0then\u00a0demonstrate\u00a0that\u00a0this\u00a0network,\u00a0trained\u00a0on\u00a0simulated\u00a0data,\u00a0can\u00a0reliably\u00a0predict\u00a0observed\u00a0real-world\u00a0transitions\u00a0in\u00a0systems\u00a0ranging\u00a0from\u00a0rapid\u00a0climatic\u00a0change\u00a0to\u00a0the\u00a0collapse\u00a0of\u00a0ecological\u00a0populations.\u00a0Importantly,\u00a0our\u00a0model\u00a0appears\u00a0to\u00a0capture\u00a0latent\u00a0properties\u00a0in\u00a0time\u00a0series\u00a0missed\u00a0by\u00a0previous\u00a0warning\u00a0signals\u00a0approaches,\u00a0allowing\u00a0us\u00a0to\u00a0not\u00a0only\u00a0detect\u00a0if\u00a0a\u00a0transition\u00a0is\u00a0approaching,\u00a0but\u00a0critically\u00a0whether\u00a0the\u00a0collapse\u00a0will\u00a0be\u00a0catastrophic\u00a0or\u00a0non-catastrophic.\u00a0These\u00a0novel\u00a0properties\u00a0mean\u00a0EWSNet\u00a0has\u00a0the\u00a0potential\u00a0to\u00a0serve\u00a0as\u00a0an\u00a0indicator\u00a0of\u00a0transitions\u00a0across\u00a0a\u00a0broad\u00a0spectrum\u00a0of\u00a0complex\u00a0systems,\u00a0without\u00a0requiring\u00a0information\u00a0on\u00a0the\u00a0structure\u00a0of\u00a0the\u00a0system\u00a0being\u00a0monitored.\u00a0Our\u00a0work\u00a0highlights\u00a0the\u00a0practicality\u00a0of\u00a0deep\u00a0learning\u00a0for\u00a0addressing\u00a0further\u00a0questions\u00a0pertaining\u00a0to\u00a0ecosystem\u00a0collapse\u00a0and\u00a0has\u00a0much\u00a0broader\u00a0management\u00a0implications.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this talk, we discuss the paper “Machine learning methods trained on simple models can predict critical transitions in complex natural systems” by\u00a0 Smita Deb, Sahil Sidheekh, Christopher F. Clements, … <\/p>\n

Continue reading “Machine learning methods trained on simple models can predict critical transitions in complex natural systems – Shingo Gibo”<\/span><\/a><\/p>\n","protected":false},"author":11,"featured_media":0,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[219],"class_list":["post-11146","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-journal-club","cat_journal-club"],"yoast_head":"\nMachine learning methods trained on simple models can predict critical transitions in complex natural systems - Shingo Gibo - Biomedical Mathematics Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/machine-learning-methods-trained-on-simple-models-can-predict-critical-transitions-in-complex-natural-systems-shingo-gibo\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine learning methods trained on simple models can predict critical transitions in complex natural systems - Shingo Gibo - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"In this talk, we discuss the paper “Machine learning methods trained on simple models can predict critical transitions in complex natural systems” by\u00a0 Smita Deb, Sahil Sidheekh, Christopher F. 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