{"id":313,"date":"2025-07-30T19:31:54","date_gmt":"2025-07-30T19:31:54","guid":{"rendered":"https:\/\/dev.www.purdue.edu\/research\/methodsmuri\/?page_id=313"},"modified":"2025-07-30T20:04:05","modified_gmt":"2025-07-30T20:04:05","slug":"products","status":"publish","type":"page","link":"https:\/\/www.purdue.edu\/research\/methodsmuri\/products\/","title":{"rendered":"METHODS MURI | Products"},"content":{"rendered":"\n
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Machine Learning Enabled Two-Phase Flow Metrologies, Models, and Optimized Designs<\/strong><\/p>\n<\/div>\n\n\n\n

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An Office of Naval Research (ONR) <\/strong>
Multidisciplinary University Research Initiative (MURI)<\/strong><\/p>\n<\/div>\n\n\n\n

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Publications<\/strong>

S. Khodakarami, V. Oommen, A. Bora, G.E. Karniadakis. Mitigating spectral bias in neural operators via high-frequency scaling for physical systems. 2025. https:\/\/arxiv.org\/abs\/2503.13695<\/a><\/p>\n\n\n\n

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Conference Presentations<\/strong>

S. Khodakarami, V. Oommen, A. Bora, G.E. Karniadakis. Mitigating spectral bias in neural operators with high-frequency scaling. 18th USNCCM. July 20-24, 2025. (Oral Presentation)

J. Hunter West, Alexander Ceperley, Vardhan Vydyula, Justin A. Weibel. Synchronous Through-Substrate High-Speed Visual and Infrared Observation of Flow Boiling in a Rectangular Channel. ASME SHTC 2025. July 8-10, 2025. (Oral Presentation)

K. N. R. Sinha, P. V. Vydyula, J. A. Weibel. A deep learning approach for heat flux partitioning analysis of pool boiling using through-substrate infrared thermography. ASME SHTC 2025. July 8-10, 2025. (Oral Presentation)

P.V. Vydyula, M. Bongarala, J.A. Weibel. Extrapolated Prediction of Pool Boiling Critical Heat Flux Through Modeling the Local Maximum in the Nucleate Boiling Curve. ASME SHTC 2025. July 8-10, 2025. (Oral Presentation)

P.V. Vydyula, M. Bongarala, J.A. Weibel. A Modeling Framework for Nucleate Pool Boiling Based on Heat Flux Partitioning. ASME SHTC 2025. July 8-10, 2025. (Poster Presentation)

Aaditya Sakrikar, Raghav Rajeev, Satish Kumar. Physics-informed Neural Network for bubble rise in two-phase flows. Micro Flow and Interfacial Phenomena. June 16-18, 2025. (Oral Presentation)

F. Naderi, T.H. Phan, L. Pirnstill, and C.R. Kharangate. Physics-Informed Neural Network Framework for Inverse Modeling of Two-Phase Boiling. Micro Flow and Interfacial Phenomena. June 16-18, 2025. (Poster Presentation)

Allison Davis, Michael Spadaro, Xiaoyu Ji, Yezhi Zhen, Minami Yoda, Fengqing Zhu. Noise-free Structured-Illumination Reconstruction of Pool Boiling Images. CVPR 2025 CV4Science Workshop. June 11-15, 2025. (Poster Presentation)

Thanh Hoang Phan, Logan Pirnstill, Jiayuan Li, Chirag Kharangate. Predicting Flow Boiling Heat Transfer Coefficient Utilizing Physics-Informed Machine Learning Model. IEEE ITherm 2025. May 27-30, 2025. (Poster Presentation)

Forouzan Naderi, Farshad Barghi Golezani, Chirag Kharangate. Enhancing Thermal Management through Deep Learning-Based Analysis of Bubble Dynamics in Flow Boiling. IEEE ITherm 2025 Conference. May 27-30, 2025. (Poster Presentation)<\/p>\n\n\n\n

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Machine Learning Enabled Two-Phase Flow Metrologies, Models, and Optimized Designs An Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI) Publications S. Khodakarami, V. Oommen, A. Bora, G.E. Karniadakis. Mitigating spectral bias in neural operators via high-frequency scaling for<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-313","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/pages\/313","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/comments?post=313"}],"version-history":[{"count":11,"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/pages\/313\/revisions"}],"predecessor-version":[{"id":341,"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/pages\/313\/revisions\/341"}],"wp:attachment":[{"href":"https:\/\/www.purdue.edu\/research\/methodsmuri\/wp-json\/wp\/v2\/media?parent=313"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}