[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121054-en":3,"doc-seo-121054-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121054,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies","The paper presents a methodology to strengthen inverse design optimization under limited computing resources by combining multi-fidelity evaluations, machine learning, and optimization strategies. It is tested on two engineering inverse design tasks, airfoil inverse design and scalar field reconstruction. A machine learning model trained on low-fidelity simulation data predicts target variables within each optimization cycle and determines when high-fidelity simulations are required, reducing expensive evaluations. The model is also used before optimization to compress design-space boundaries, accelerating convergence. The approach improves Differential Evolution and Particle Swarm Optimization and generalizes to other population-based optimizers.","Lawrence Berkeley National Laboratory LBL Publications  \nTitle  \nEfficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies  \nPermalink  \n[https://escholarship.org/uc/item/6k289101](https://escholarship.org/uc/item/6k289101)  \nAuthors  \nGrbcic, Luka  \nMüller, Juliane  \nde Jong, Wibe Albert  \nPublication Date  \n2024  \nDOI  \n10.1007/s00366-024-02053-4  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nEngineering with Computers  \n[https://doi.org/10.1007/s00366-024-02053-4](https://doi.org/10.1007/s00366-024-02053-4)  \nEfficient inverse design optimization through multi‑fidelity  \nsimulations, machine learning, and boundary refinement strategies  \nLuka Grbcic1 · Juliane Müller2 · Wibe Albert de Jong1  \nReceived: 4 December 2023 / Accepted: 22 August 2024 © The Author(s) 2024  \nAbstract  \nThis paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute, through the strategic synergy of multi-fidelity evaluations, machine learning models, and optimization algorithms. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. It leverages a machine learning model trained with low-fidelity simulation data, in each optimization cycle, thereby proficiently predicting a target variable and discerning whether a high-fidelity simulation is necessitated, which notably conserves computational resources. Additionally, the machine learning model is strategically deployed prior to optimization to compress the design space boundaries, thereby further accelerating convergence toward the optimal solution. The methodology has been employed to enhance two optimization algorithms, namely Differential Evolution and Particle Swarm Optimization. Comparative analyses illustrate performance improvements across both algorithms. Notably, this method is adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.  \nKeywords Multi-fidelity optimization · Machine learning · Inverse design · Particle swarm optimization · Differential evolution  \n1 Introduction  \nInverse design problems represent a frontier in the field of engineering and science, where the objective is to discover the necessary system inputs to achieve a desired known output. Rather than following the traditional forward design process–which starts with given parameters and attempts to predict the outcome–inverse design turns the procedure on  \n* Luka Grbcic[lgrbcic@lbl.gov](lgrbcic@lbl.gov)  \nJuliane Müller  \n[juliane.mueller@nrel.gov](juliane.mueller@nrel.gov)  \nWibe Albert de Jong  \n[wadejong@lbl.gov](wadejong@lbl.gov)  \n1 Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA  \n2 Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA  \nits head, beginning with the desired outcome and working backward to determine the optimal parameters to realize it. Particularly in scenarios with computationally expensive or hierarchical simulations, multi-fidelity evaluations playa pivotal role, offering a trade-off between accuracy and computational cost.  \nMulti-fidelity (MF) methods, that range from faster and approximate or low-fidelity (LF) objective function evaluations to detailed–high fidelity (HF), computationally intensive ones have been explored i","cbCaia4zM36aAh9e","https://ap.wps.com/l/cbCaia4zM36aAh9e","pdf",6801404,1,29,"English","en",105,"# Introduction\n## Inverse design and computational cost\n## Multi-fidelity methods in inverse optimization\n## Variable-fidelity strategies and surrogate models\n## Variable-fidelity optimization mechanisms","[{\"question\":\"What is the main goal of the proposed methodology?\",\"answer\":\"To enhance inverse design optimization when compute is limited by combining multi-fidelity simulations, machine learning, and optimization strategies to reduce expensive evaluations and speed convergence.\"},{\"question\":\"Which engineering problems are used to evaluate the method?\",\"answer\":\"Airfoil inverse design and scalar field reconstruction.\"},{\"question\":\"How does the machine learning model reduce computational expense?\",\"answer\":\"It is trained on low-fidelity simulation data and predicts target variables during optimization cycles while indicating whether a high-fidelity simulation is necessary, thereby conserving resources.\"}]","Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies | PDF",1785733506,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"efficient-inverse-design-optimization-through-multi-fidelity-simulations-machine-learning-and-boundary-refinement-strategies","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/efficient-inverse-design-optimization-through-multi-fidelity-simulations-machine-learning-and-boundary-refinement-strategies/121054/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed methodology?","Question",{"text":75,"@type":76},"To enhance inverse design optimization when compute is limited by combining multi-fidelity simulations, machine learning, and optimization strategies to reduce expensive evaluations and speed convergence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which engineering problems are used to evaluate the method?",{"text":80,"@type":76},"Airfoil inverse design and scalar field reconstruction.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the machine learning model reduce computational expense?",{"text":84,"@type":76},"It is trained on low-fidelity simulation data and predicts target variables during optimization cycles while indicating whether a high-fidelity simulation is necessary, thereby conserving resources.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]