[{"@context":"https:\/\/schema.org\/","@type":"BlogPosting","@id":"https:\/\/blog.terabox.com\/insights\/rare-event-analysis-stochastic-optimal-control#BlogPosting","mainEntityOfPage":"https:\/\/blog.terabox.com\/insights\/rare-event-analysis-stochastic-optimal-control","headline":"Rare Event Analysis vs Stochastic Optimal Control","name":"Rare Event Analysis vs Stochastic Optimal Control","description":"\ud83d\udcfa Today’s recommended deep-dive video: https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos Solving the Impossible: Mastering Rare Events Through Stochastic Optimal ControlThe Rarity Problem and the CommittorControl as the CatalystBridging the Gap with Value MatchingKey TakeawaysQ&A Solving the Impossible: Mastering Rare Events Through Stochastic Optimal Control... ","datePublished":"2026-08-14","dateModified":"2026-08-14","author":{"@type":"Person","@id":"https:\/\/blog.terabox.com\/author\/flextech-admin\/#Person","name":"flextech-admin","url":"https:\/\/blog.terabox.com\/author\/flextech-admin\/","image":{"@type":"ImageObject","@id":"https:\/\/secure.gravatar.com\/avatar\/ad516503a11cd5ca435acc9bb6523536?s=150&d=mm&r=gforcedefault=1","url":"https:\/\/secure.gravatar.com\/avatar\/ad516503a11cd5ca435acc9bb6523536?s=150&d=mm&r=gforcedefault=1","height":96,"width":96}},"publisher":{"@type":"Organization","name":"terabox","logo":{"@type":"ImageObject","@id":"http:\/\/blog.terabox.com\/wp-content\/uploads\/2021\/11\/logo\u4ea7\u54c1\u540d-\u7ad6\u7248.png","url":"http:\/\/blog.terabox.com\/wp-content\/uploads\/2021\/11\/logo\u4ea7\u54c1\u540d-\u7ad6\u7248.png","width":900,"height":900}},"image":{"@type":"ImageObject","@id":"https:\/\/img.youtube.com\/vi\/DlJcoEu1uos\/maxresdefault.jpg","url":"https:\/\/img.youtube.com\/vi\/DlJcoEu1uos\/maxresdefault.jpg","height":"","width":""},"url":"https:\/\/blog.terabox.com\/insights\/rare-event-analysis-stochastic-optimal-control","video":{"@context":"http:\/\/schema.org\/","@type":"VideoObject","@id":"https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos#VideoObject","contentUrl":"https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos","name":"Rare event analysis via stochastic optimal control","description":"Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely difficult to study computationally because unbiased simulations seldom produce them. Transition Path Theory (TPT) provides a rigorous statistical framework for analyzing such events: it characterizes the ensemble of reactive trajectories between two designated metastable states (reactant and product), and its central object--the committor function, which gives the probability that the system will next reach the product rather than the reactant--encodes all essential kinetic and thermodynamic information. We introduce a framework that casts committor estimation as a stochastic optimal control (SOC) problem. In this formulation the committor defines a feedback control--proportional to the gradient of its logarithm--that actively steers trajectories toward the reactive region, thereby enabling efficient sampling of reactive paths. To solve the resulting hitting-time control problem we develop two complementary objectives: a direct backpropagation loss and a principled off-policy Value Matching loss, for which we establish first-order optimality guarantees. We further address metastability, which can trap controlled trajectories in intermediate basins, by introducing an alternative sampling process that preserves the reactive current while lowering effective energy barriers. On benchmark systems, the framework yields markedly more accurate committor estimates, reaction rates, and equilibrium constants than existing methods.\n\nSpeaker bios: Yuanqi Du is a Senior Research at Microsoft Research New England. He received his Ph.D. in Computer Science from Cornell University. His research focuses on developing principled and efficient probabilistic and geometric modeling methods that are inspired by, and accelerate, discovery in the natural sciences, spanning chemistry, physics, and biology.\n\nCarles Domingo-Enrich is a Senior Researcher at Microsoft Research New England. He works on generative AI models (diffusion and flow models, language models) and related topics at the intersection of machine learning, statistics, and AI for science. He received his PhD in Computer Science from NYU.\n\nFind seminar details and upcoming talks: https:\/\/www.microsoft.com\/en-us\/research\/event\/microsoft-research-new-england-generative-modeling-sampling-seminar\/","thumbnailUrl":["https:\/\/i.ytimg.com\/vi\/DlJcoEu1uos\/default.jpg","https:\/\/i.ytimg.com\/vi\/DlJcoEu1uos\/mqdefault.jpg","https:\/\/i.ytimg.com\/vi\/DlJcoEu1uos\/hqdefault.jpg","https:\/\/i.ytimg.com\/vi\/DlJcoEu1uos\/sddefault.jpg","https:\/\/i.ytimg.com\/vi\/DlJcoEu1uos\/maxresdefault.jpg"],"uploadDate":"2026-06-16T22:55:30+00:00","duration":"PT1H9M23S","embedUrl":"https:\/\/www.youtube.com\/embed\/DlJcoEu1uos","publisher":{"@type":"Organization","@id":"https:\/\/www.youtube.com\/channel\/UCCb9_Kn8F_Opb3UCGm-lILQ#Organization","url":"https:\/\/www.youtube.com\/channel\/UCCb9_Kn8F_Opb3UCGm-lILQ","name":"Microsoft Research","description":"At Microsoft Research, we accelerate scientific discovery and technology innovation to empower every person and organization on the planet to achieve more. We do this by bringing together the best minds across diverse disciplines and backgrounds to take on the most pressing research challenges for Microsoft and for society.\n\nOur Research Lens\nWe consider research directions through the lens of the positive impact we aspire to create with and for customers, communities, and all of society.\n","logo":{"url":"https:\/\/yt3.ggpht.com\/H4L6_GZ4lS3MqX3Rw9XnqAfltj1GZscDT4144uypXqNuVYhqLmUiTRiWygg7cnhMdM2nfxo9KFM=s800-c-k-c0x00ffffff-no-rj","width":800,"height":800,"@type":"ImageObject","@id":"https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos#VideoObject_publisher_logo_ImageObject"}},"potentialAction":{"@type":"SeekToAction","@id":"https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos#VideoObject_potentialAction","target":"https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos&t={seek_to_second_number}","startOffset-input":"required name=seek_to_second_number"},"interactionStatistic":[[{"@type":"InteractionCounter","@id":"https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos#VideoObject_interactionStatistic_WatchAction","interactionType":{"@type":"WatchAction"},"userInteractionCount":2206}],{"@type":"InteractionCounter","@id":"https:\/\/www.youtube.com\/watch?v=DlJcoEu1uos#VideoObject_interactionStatistic_LikeAction","interactionType":{"@type":"LikeAction"},"userInteractionCount":84}]},"about":["Insights","\u300eEnglish\u300f"],"wordCount":1489},{"@context":"https:\/\/schema.org\/","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Insights","item":"https:\/\/blog.terabox.com\/insights\/#breadcrumbitem"},{"@type":"ListItem","position":2,"name":"Rare Event Analysis vs Stochastic Optimal Control","item":"https:\/\/blog.terabox.com\/insights\/rare-event-analysis-stochastic-optimal-control#breadcrumbitem"}]}]