{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:04:06Z","timestamp":1777129446548,"version":"3.51.4"},"reference-count":24,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000015","name":"US Department of Energy","doi-asserted-by":"crossref","award":["DE-AC05-00OR22725"],"award-info":[{"award-number":["DE-AC05-00OR22725"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad7cbf","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T23:03:18Z","timestamp":1726700598000},"page":"045009","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Distance preserving machine learning for uncertainty aware accelerator capacitance predictions"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-6298","authenticated-orcid":true,"given":"Steven","family":"Goldenberg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3475-2871","authenticated-orcid":true,"given":"Malachi","family":"Schram","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4430-9937","authenticated-orcid":false,"given":"Kishansingh","family":"Rajput","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3244-041X","authenticated-orcid":false,"given":"Thomas","family":"Britton","sequence":"additional","affiliation":[]},{"given":"Chris","family":"Pappas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-9843","authenticated-orcid":true,"given":"Dan","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jared","family":"Walden","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2743-0567","authenticated-orcid":false,"given":"Majdi I","family":"Radaideh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7147-9619","authenticated-orcid":false,"given":"Sarah","family":"Cousineau","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8543-3803","authenticated-orcid":false,"given":"Sudarshan","family":"Harave","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"mlstad7cbfbib1","first-page":"pp 553","article-title":"Design, status and first operations of the spallation neutron source polyphase resonant converter modulator system","volume":"vol 1","author":"Reass","year":"2003"},{"key":"mlstad7cbfbib2","first-page":"pp 3340","article-title":"Lifetime testing of metallized thin film capacitors for inverter applications","author":"Flicker","year":"2013"},{"key":"mlstad7cbfbib3","article-title":"Analog Devices 2023 LTspice information center"},{"key":"mlstad7cbfbib4","first-page":"pp 715","article-title":"Progress on machine learning for the SNS high voltage converter modulators","author":"Radaideh","year":"2022"},{"key":"mlstad7cbfbib5","first-page":"pp 9690","article-title":"Uncertainty estimation using a single deep deterministic neural network","author":"Van Amersfoort","year":"2020"},{"key":"mlstad7cbfbib6","article-title":"On feature collapse and deep kernel learning for single forward pass uncertainty","author":"Van Amersfoort","year":"2021"},{"key":"mlstad7cbfbib7","article-title":"Random features for large-scale kernel machines","volume":"vol 20","author":"Rahimi","year":"2007"},{"key":"mlstad7cbfbib8","first-page":"pp 7498","article-title":"Simple and principled uncertainty estimation with deterministic deep learning via distance awareness","volume":"vol 33","author":"Liu","year":"2020"},{"key":"mlstad7cbfbib9","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevAccelBeams.25.122802","article-title":"Uncertainty aware anomaly detection to predict errant beam pulses in the Oak Ridge Spallation Neutron Source accelerator","volume":"25","author":"Blokland","year":"2022","journal-title":"Phys. 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