{"689932":{"#nid":"689932","#data":{"type":"news","title":"Vision AI Models Improve Decision Making in Manufacturing, Energy, and Finance","body":[{"value":"\u003Cp\u003EGenerative artificial intelligence (AI) is best known for creating images and text. Now, it is helping industries make better planning decisions.\u003C\/p\u003E\u003Cp\u003EGeorgia Tech researchers have created a new AI model for decision-focused learning (DFL), called Diffusion-DFL. Recent tests showed it makes more accurate decisions than current approaches.\u003C\/p\u003E\u003Cp\u003EAlong with optimizing industrial output, Diffusion-DFL lowers costs and reduces risk. Experiments also showed it performs across different fields.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/2510.11590\u0022\u003E\u003Cstrong\u003EDiffusion-DFL\u003C\/strong\u003E\u003C\/a\u003E doesn\u2019t just surpass current methods; it also predicts more accurately as problem sizes grow. The model requires less computing power despite these high-performance marks, making it more accessible to smaller enterprises.\u003C\/p\u003E\u003Cp\u003EDiffusion-DFL runs on diffusion models, the same technology that powers DALL-E and other AI image generators. It is the first DFL framework based on diffusion models.\u003C\/p\u003E\u003Cp\u003E\u201cAnyone who makes high-stakes decisions under uncertainty, including supply chain managers, energy operators, and financial planners, benefits from Diffusion-DFL,\u201d said\u0026nbsp;\u003Ca href=\u0022https:\/\/www.zihaozhao.site\/\u0022\u003E\u003Cstrong\u003EZihao Zhao\u003C\/strong\u003E\u003C\/a\u003E, a Georgia Tech Ph.D. student who led the project.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cInstead of optimizing around a single forecast, the model evaluates many possible scenarios, so decisions account for real-world risk and become more robust.\u201d\u003C\/p\u003E\u003Cp\u003E[\u003Ca href=\u0022https:\/\/sites.gatech.edu\/research\/iclr-2026\/\u0022\u003E\u003Cstrong\u003ERelated: GT @ ICLR 2026\u003C\/strong\u003E\u003C\/a\u003E]\u003C\/p\u003E\u003Cp\u003ETo test Diffusion-DFL, the team ran experiments based on real-world settings, including:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EFactory manufacturing to meet product demand\u003C\/li\u003E\u003Cli\u003EPower grid scheduling to meet energy demand\u003C\/li\u003E\u003Cli\u003EStock market portfolio optimization\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EIn each case, Diffusion-DFL made more accurate decisions than current methods. It also performed better as problems became larger and more complex. These results confirm the model\u2019s ability to make important decisions in real-world scenarios with noisy data and uncertainty.\u003C\/p\u003E\u003Cp\u003EThe experiments also show that Diffusion-DFL is practical, not just accurate. Training diffusion models is expensive, so the team developed a way to reduce memory use. This cut training costs by more than 99.7%. As a result, Diffusion-DFL can reach more researchers and practitioners.\u003C\/p\u003E\u003Cp\u003E\u201cOur score-function estimator cuts GPU memory from over 60 gigabytes to 0.13 with almost no loss in decision quality, reducing the requirement for massive computing resources,\u201d Zhao said. \u201cI hope this expands Diffusion-DFL into other domains, like healthcare, where decisions must be made quickly under complex uncertainty.\u0022\u003C\/p\u003E\u003Cp\u003EBeyond decision-making applications, Diffusion-DFL marks a shift in DFL techniques and in the broader use of generative AI models.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn supply chain management, planners estimate future demand before deciding how much product to stock. In this DFL problem, engineers align ML models with predetermined decision objectives, like minimizing risk or reducing costs.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EOne flaw of DFL methods is that they optimize around a single, deterministic prediction in an uncertain future.\u003C\/p\u003E\u003Cp\u003EDiffusion-DFL takes a different approach. Instead of making a single guess, it determines a range of possible outcomes. This leads to decisions based on many likely scenarios, rather than on a single assumed future.\u003C\/p\u003E\u003Cp\u003ETo do this, the framework uses diffusion models. These generative AI models create high-quality data from images, text, and audio.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe forward diffusion process involves adding noise to data until it becomes pure noise. Models trained via forward diffusion can reverse diffusion. This means they can start with noisy data and then produce meaningful insights from training examples.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EReal-world data is often noisy and uncertain. Traditional DFL methods struggle in these conditions, but diffusion models are designed to handle them.\u003C\/p\u003E\u003Cp\u003EBecause of this, Diffusion-DFL can explore many possible outcomes and choose better actions. Like image-generation AI, the model works well with complex data from different sources. This enables its use across different industries.\u003C\/p\u003E\u003Cp\u003E\u201cDiffusion models have achieved significant success in generative AI and image synthesis, but our work shows their potential extends far beyond that,\u201d said\u0026nbsp;\u003Ca href=\u0022https:\/\/guaguakai.com\/\u0022\u003E\u003Cstrong\u003EKai Wang\u003C\/strong\u003E\u003C\/a\u003E, an assistant professor in the\u0026nbsp;\u003Ca href=\u0022https:\/\/cse.gatech.edu\/\u0022\u003E\u003Cstrong\u003ESchool of Computational Science and Engineering\u003C\/strong\u003E\u003C\/a\u003E (CSE).\u003C\/p\u003E\u003Cp\u003E\u201cWhat makes Diffusion-DFL unique is that the specific downstream application guides how the model learns to handle uncertainty.\u003C\/p\u003E\u003Cp\u003E\u201cWhether we are scheduling energy for power grids, balancing risk in financial portfolios, or developing early warning systems in healthcare, we can explicitly train these highly expressive models to navigate the unique complexities of each domain.\u201d\u003C\/p\u003E\u003Cp\u003EZhao and Wang collaborated with Caltech Ph.D. candidate\u0026nbsp;\u003Ca href=\u0022https:\/\/chrisyeh96.github.io\/\u0022\u003E\u003Cstrong\u003EChristopher Yeh\u003C\/strong\u003E\u003C\/a\u003E and Harvard University postdoctoral fellow\u0026nbsp;\u003Ca href=\u0022https:\/\/www.cc.gatech.edu\/news\/alumnus-uses-ai-counter-african-poaching-improve-maternal-healthcare-access\u0022\u003E\u003Cstrong\u003ELingkai Kong\u003C\/strong\u003E\u003C\/a\u003E on Diffusion-DFL. Kong earned his Ph.D. in CSE from Georgia Tech in 2024.\u003C\/p\u003E\u003Cp\u003EWang will present Diffusion-DFL on behalf of the group at the upcoming International Conference on Learning Representations (\u003Ca href=\u0022https:\/\/iclr.cc\/\u0022\u003E\u003Cstrong\u003EICLR 2026\u003C\/strong\u003E\u003C\/a\u003E). Occurring April 23-27 in Rio de Janeiro, ICLR is one of the world\u2019s most prestigious conferences dedicated to artificial intelligence research.\u003C\/p\u003E\u003Cp\u003E\u201cICLR is the perfect stage for Diffusion-DFL because it brings together the exact community that needs to see the bridge between generative modeling and high-stakes decision-making for real-world applications,\u201d Wang said.\u003C\/p\u003E\u003Cp\u003E\u201cPresenting Diffusion-DFL allows us to challenge the traditional training framework of diffusion models. It\u2019s about sparking a broader conversation on how we can align the training objectives of generative AI directly with actual, downstream decision-making needs.\u201d\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGenerative artificial intelligence (AI) is best known for creating images and text. Now, it is helping industries make better planning decisions.\u003C\/p\u003E\u003Cp\u003EGeorgia Tech researchers have created a new AI model for decision-focused learning (DFL), called Diffusion-DFL. Recent tests showed it makes more accurate decisions than current approaches.\u003C\/p\u003E\u003Cp\u003EAlong with optimizing industrial output, Diffusion-DFL lowers costs and reduces risk. Experiments also showed it performs across different fields.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/2510.11590\u0022\u003E\u003Cstrong\u003EDiffusion-DFL\u003C\/strong\u003E\u003C\/a\u003E doesn\u2019t just surpass current methods; it also predicts more accurately as problem sizes grow. The model requires less computing power despite these high-performance marks, making it more accessible to smaller enterprises.\u003C\/p\u003E\u003Cp\u003EDiffusion-DFL runs on diffusion models, the same technology that powers DALL-E and other AI image generators. It is the first DFL framework based on diffusion models.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia Tech researchers have developed Diffusion-DFL, the first decision-focused learning model built on diffusion AI technology. It uses the same engineering behind image generators to help industries make more accurate, lower-cost planning decisions."}],"uid":"36319","created_gmt":"2026-04-21 17:35:24","changed_gmt":"2026-04-21 17:40:39","author":"Bryant Wine","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2026-04-15T00:00:00-04:00","iso_date":"2026-04-15T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"680015":{"id":"680015","type":"image","title":"Diffusion-DFL-Head-Image.jpg","body":null,"created":"1776792936","gmt_created":"2026-04-21 17:35:36","changed":"1776792936","gmt_changed":"2026-04-21 17:35:36","alt":"ICLR 2026 Diffusion-DFL","file":{"fid":"264248","name":"Diffusion-DFL-Head-Image.jpg","image_path":"\/sites\/default\/files\/2026\/04\/21\/Diffusion-DFL-Head-Image.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/04\/21\/Diffusion-DFL-Head-Image.jpg","mime":"image\/jpeg","size":117435,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/04\/21\/Diffusion-DFL-Head-Image.jpg?itok=2myOXxFR"}}},"media_ids":["680015"],"related_links":[{"url":"https:\/\/www.cc.gatech.edu\/news\/vision-ai-models-improve-decision-making-manufacturing-energy-and-finance","title":"Vision AI Models Improve Decision Making in Manufacturing, Energy, and Finance"}],"groups":[{"id":"1188","name":"Research Horizons"}],"categories":[{"id":"194606","name":"Artificial Intelligence"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"131","name":"Economic Development and Policy"},{"id":"144","name":"Energy"},{"id":"194609","name":"Industry"},{"id":"194685","name":"Manufacturing"},{"id":"135","name":"Research"},{"id":"8862","name":"Student Research"}],"keywords":[{"id":"187812","name":"artificial intelligence (AI)"},{"id":"10199","name":"Daily Digest"},{"id":"181991","name":"Georgia Tech News Center"},{"id":"9167","name":"machine learning"},{"id":"181689","name":"Institute for Data Science and Engineering"},{"id":"187915","name":"go-researchnews"},{"id":"9153","name":"Research Horizons"},{"id":"194384","name":"Tech AI"},{"id":"7850","name":"EVPR"}],"core_research_areas":[{"id":"193655","name":"Artificial Intelligence at Georgia Tech"},{"id":"39431","name":"Data Engineering and Science"},{"id":"39461","name":"Manufacturing, Trade, and Logistics"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EBryant Wine, Communications Officer\u003Cbr\u003E\u003Ca href=\u0022mailto:bryant.wine@cc.gatech.edu\u0022\u003Ebryant.wine@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}