{"639658":{"#nid":"639658","#data":{"type":"event","title":"PhD Defense by Dushhyanth Rajaram","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EDushhyanth Rajaram\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cem\u003E(Advisor: Prof. Dimitri Mavris)\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003Ewill defend a doctoral thesis entitled,\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMethods for Construction of Surrogates For Computationally Expensive High-Dimensional Problems\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETuesday, October 13\u003Csup\u003Eth\u003C\/sup\u003E at 11:00 a.m.\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMeeting Link: \u003C\/strong\u003E\u003Ca href=\u0022https:\/\/bluejeans.com\/472745898\u0022\u003E\u003Cstrong\u003Ehttps:\/\/bluejeans.com\/472745898\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003Cbr \/\u003E\r\nMaturation of computational models has increased reliance on numerical simulations for the analysis, and more importantly, design of complex engineered systems. The high accuracy and realism offered by simulation-based analysis often comes at a high computational cost especially in the \u003Cem\u003Emany-query\u003C\/em\u003E context, as such limiting its applicability in exploratory design studies. In the absence of inexpensive models that exploit physics-based simplifying assumptions, practitioners often resort to computationally cheap surrogate-based methods. However, several challenges arise when constructing surrogates for high-dimensional field outputs. Identifying and tackling these issues is the primary goal of this dissertation. The challenges posed by the following three key issues are investigated: 1) the need to handle large datasets under constrained computational resources, 2) the presence of a large number of inputs, 3) the need for accurate models under scarcity of data from expensive simulations with many inputs.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPursuit of the first issue investigates the viability of randomization as a means to perform computationally efficient data compression while retaining sufficient accuracy to construct surrogate models for large field responses.\u0026nbsp; Accommodation of a large number of inputs is tackled through the formulation of a manifold optimization-based Gaussian Process (MO-GP) regression model that simultaneously finds a low-dimensional input subspace and trains a model in it using input-output pairs exclusively. To emulate field outputs using the Proper Orthogonal Decomposition (POD) and interpolation for analyses with a large number of inputs, the MO-GP model is leveraged to learn a map from the inputs to each POD coordinate. Finally, the use of a multifidelity extension to the MO-GP model in conjunction with a recently proposed manifold alignment-based model is proposed as a solution to improve predictive accuracy with insufficient high-fidelity data.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EFindings show that: 1) randomization enables efficient construction of competitive predictive models under constrained computational resources, 2) the MO-GP model is effective in finding a low-dimensional input subspace for each POD coordinate and results in a good predictive model, and 3) an initial feasibility assessment of the multifidelity model on an airfoil flow emulation problem shows promise but warrants further investigation.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EProf. Dimitri Mavris \u0026ndash; School of Aerospace Engineering (Advisor and Committee Chair)\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EProf. Jechiel Jagoda \u0026ndash; School of Aerospace Engineering\u003C\/li\u003E\r\n\t\u003Cli\u003EProf. \u0026Uuml;mit V. \u0026Ccedil;ataly\u0026uuml;rek \u0026ndash; School of Computational Science and Engineering\u003C\/li\u003E\r\n\t\u003Cli\u003EDr. Olivia Pinon Fischer \u0026ndash; Senior Research Engineer, School of Aerospace Engineering\u003C\/li\u003E\r\n\t\u003Cli\u003EDr. Frederic Villeneuve \u0026ndash; Principal Systems Lead, Blue Origin\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Methods for Construction of Surrogates For Computationally Expensive High-Dimensional Problems"}],"uid":"27707","created_gmt":"2020-09-28 19:04:39","changed_gmt":"2020-09-28 19:04:39","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-10-13T12:00:00-04:00","event_time_end":"2020-10-13T14:00:00-04:00","event_time_end_last":"2020-10-13T14:00:00-04:00","gmt_time_start":"2020-10-13 16:00:00","gmt_time_end":"2020-10-13 18:00:00","gmt_time_end_last":"2020-10-13 18:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}