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Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin\u2013spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. The associated notebooks allow to reproduce the reported analyses and apply them to other systems.2<\/jats:p>","DOI":"10.1115\/1.4066856","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T09:52:38Z","timestamp":1728899558000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":2,"title":["Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization"],"prefix":"10.1115","volume":"24","author":[{"given":"Arpan","family":"Biswas","sequence":"first","affiliation":[{"name":"University of Tennessee-Oak Ridge Innovation Institute , University of Tennessee, Knoxville, TN 37923"}]},{"given":"Mani","family":"Valleti","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00xzqjh13","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Tennessee System Bredesen Center for Interdisciplinary Research, , Knoxville, TN 37996"},{"name":"University of Tennessee Bredesen Center for Interdisciplinary Research, , Knoxville, TN 37996"}]},{"given":"Rama","family":"Vasudevan","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory Center for Nanophase Materials Sciences, , Oak Ridge, TN 37830"}]},{"given":"Maxim","family":"Ziatdinov","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory Physical Sciences Division, , Richland, WA 99352"}]},{"given":"Sergei V.","family":"Kalinin","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/020f3ap87","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Tennessee at Knoxville Physical Sciences Division, , Richland, WA 99352 ; Department of Materials Science and Engineering, , Knoxville, TN 37996"},{"name":"Pacific Northwest National Laboratory Physical Sciences Division, , Richland, WA 99352 ; Department of Materials Science and Engineering, , Knoxville, TN 37996"},{"name":"University of Tennessee Physical Sciences Division, , Richland, WA 99352 ; Department of Materials Science and Engineering, , Knoxville, TN 37996"}]}],"member":"33","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"issue":"18","key":"2025110420014966300_CIT0001","doi-asserted-by":"publisher","first-page":"184410","DOI":"10.1103\/PhysRevB.96.184410","article-title":"Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory","volume":"96","author":"Wetzel","year":"2017","journal-title":"Phys. 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