{"file":[{"date_updated":"2022-02-22T07:20:12Z","relation":"source_file","file_size":17668233,"file_id":"10785","date_created":"2022-02-21T13:58:16Z","file_name":"Rzadkowski_thesis_final_source.zip","checksum":"0fc54ad1eaede879c665ac9b53c93e22","access_level":"closed","content_type":"application/zip","creator":"wrzadkow"},{"date_created":"2022-02-21T14:02:54Z","file_id":"10786","file_size":13307331,"relation":"main_file","date_updated":"2022-02-21T14:02:54Z","creator":"wrzadkow","content_type":"application/pdf","success":1,"access_level":"open_access","checksum":"22d2d7af37ca31f6b1730c26cac7bced","file_name":"Rzadkowski_thesis_final.pdf"}],"_id":"10759","status":"public","publication_status":"published","department":[{"_id":"GradSch"},{"_id":"MiLe"}],"article_processing_charge":"No","ddc":["530"],"type":"dissertation","alternative_title":["ISTA Thesis"],"date_published":"2022-02-21T00:00:00Z","title":"Analytic and machine learning approaches to composite quantum impurities","project":[{"name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"}],"file_date_updated":"2022-02-22T07:20:12Z","date_created":"2022-02-16T13:27:37Z","related_material":{"record":[{"status":"public","id":"10762","relation":"part_of_dissertation"},{"id":"7956","status":"public","relation":"part_of_dissertation"},{"relation":"part_of_dissertation","id":"415","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"8644"}]},"citation":{"short":"W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum Impurities, Institute of Science and Technology Austria, 2022.","ama":"Rzadkowski W. Analytic and machine learning approaches to composite quantum impurities. 2022. doi:<a href=\"https://doi.org/10.15479/at:ista:10759\">10.15479/at:ista:10759</a>","chicago":"Rzadkowski, Wojciech. “Analytic and Machine Learning Approaches to Composite Quantum Impurities.” Institute of Science and Technology Austria, 2022. <a href=\"https://doi.org/10.15479/at:ista:10759\">https://doi.org/10.15479/at:ista:10759</a>.","apa":"Rzadkowski, W. (2022). <i>Analytic and machine learning approaches to composite quantum impurities</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:10759\">https://doi.org/10.15479/at:ista:10759</a>","mla":"Rzadkowski, Wojciech. <i>Analytic and Machine Learning Approaches to Composite Quantum Impurities</i>. Institute of Science and Technology Austria, 2022, doi:<a href=\"https://doi.org/10.15479/at:ista:10759\">10.15479/at:ista:10759</a>.","ista":"Rzadkowski W. 2022. Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria.","ieee":"W. Rzadkowski, “Analytic and machine learning approaches to composite quantum impurities,” Institute of Science and Technology Austria, 2022."},"month":"02","author":[{"orcid":"0000-0002-1106-4419","id":"48C55298-F248-11E8-B48F-1D18A9856A87","first_name":"Wojciech","last_name":"Rzadkowski","full_name":"Rzadkowski, Wojciech"}],"language":[{"iso":"eng"}],"year":"2022","date_updated":"2024-08-07T07:16:53Z","day":"21","publication_identifier":{"issn":["2663-337X"]},"doi":"10.15479/at:ista:10759","ec_funded":1,"oa_version":"Published Version","page":"120","supervisor":[{"orcid":"0000-0002-6990-7802","first_name":"Mikhail","last_name":"Lemeshko","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","full_name":"Lemeshko, Mikhail"}],"publisher":"Institute of Science and Technology Austria","abstract":[{"lang":"eng","text":"In this Thesis, I study composite quantum impurities with variational techniques, both inspired by machine learning as well as fully analytic. I supplement this with exploration of other applications of machine learning, in particular artificial neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle systems with variational approach. I derive a Hamiltonian describing the angulon quasiparticle in the presence of a magnetic field. I apply analytic variational treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive systems, based on artificial neural networks. I exemplify this approach on the example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue using artificial neural networks, albeit in a different setting. I apply artificial neural networks to detect phases from snapshots of two types physical systems. Namely, I study Monte Carlo snapshots of multilayer classical spin models as well as molecular dynamics maps of colloidal systems. The main type of networks that I use here are convolutional neural networks, known for their applicability to image data."}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","degree_awarded":"PhD","has_accepted_license":"1","oa":1}