--- _id: '11473' abstract: - lang: eng text: "The polaron model is a basic model of quantum field theory describing a single particle\r\ninteracting with a bosonic field. It arises in many physical contexts. We are mostly concerned\r\nwith models applicable in the context of an impurity atom in a Bose-Einstein condensate as\r\nwell as the problem of electrons moving in polar crystals.\r\nThe model has a simple structure in which the interaction of the particle with the field is given\r\nby a term linear in the field’s creation and annihilation operators. In this work, we investigate\r\nthe properties of this model by providing rigorous estimates on various energies relevant to the\r\nproblem. The estimates are obtained, for the most part, by suitable operator techniques which\r\nconstitute the principal mathematical substance of the thesis.\r\nThe first application of these techniques is to derive the polaron model rigorously from first\r\nprinciples, i.e., from a full microscopic quantum-mechanical many-body problem involving an\r\nimpurity in an otherwise homogeneous system. We accomplish this for the N + 1 Bose gas\r\nin the mean-field regime by showing that a suitable polaron-type Hamiltonian arises at weak\r\ninteractions as a low-energy effective theory for this problem.\r\nIn the second part, we investigate rigorously the ground state of the model at fixed momentum\r\nand for large values of the coupling constant. Qualitatively, the system is expected to display\r\na transition from the quasi-particle behavior at small momenta, where the dispersion relation\r\nis parabolic and the particle moves through the medium dragging along a cloud of phonons, to\r\nthe radiative behavior at larger momenta where the polaron decelerates and emits free phonons.\r\nAt the same time, in the strong coupling regime, the bosonic field is expected to behave purely\r\nclassically. Accordingly, the effective mass of the polaron at strong coupling is conjectured to\r\nbe asymptotically equal to the one obtained from the semiclassical counterpart of the problem,\r\nfirst studied by Landau and Pekar in the 1940s. For polaron models with regularized form\r\nfactors and phonon dispersion relations of superfluid type, i.e., bounded below by a linear\r\nfunction of the wavenumbers for all phonon momenta as in the interacting Bose gas, we prove\r\nthat for a large window of momenta below the radiation threshold, the energy-momentum\r\nrelation at strong coupling is indeed essentially a parabola with semi-latus rectum equal to the\r\nLandau–Pekar effective mass, as expected.\r\nFor the Fröhlich polaron describing electrons in polar crystals where the dispersion relation is\r\nof the optical type and the form factor is formally UV–singular due to the nature of the point\r\ncharge-dipole interaction, we are able to give the corresponding upper bound. In contrast to\r\nthe regular case, this requires the inclusion of the quantum fluctuations of the phonon field,\r\nwhich makes the problem considerably more difficult.\r\nThe results are supplemented by studies on the absolute ground-state energy at strong coupling,\r\na proof of the divergence of the effective mass with the coupling constant for a wide class of\r\npolaron models, as well as the discussion of the apparent UV singularity of the Fröhlich model\r\nand the application of the techniques used for its removal for the energy estimates.\r\n" acknowledged_ssus: - _id: SSU alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Krzysztof full_name: Mysliwy, Krzysztof id: 316457FC-F248-11E8-B48F-1D18A9856A87 last_name: Mysliwy citation: ama: 'Mysliwy K. Polarons in Bose gases and polar crystals: Some rigorous energy estimates. 2022. doi:10.15479/at:ista:11473' apa: 'Mysliwy, K. (2022). Polarons in Bose gases and polar crystals: Some rigorous energy estimates. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11473' chicago: 'Mysliwy, Krzysztof. “Polarons in Bose Gases and Polar Crystals: Some Rigorous Energy Estimates.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11473.' ieee: 'K. Mysliwy, “Polarons in Bose gases and polar crystals: Some rigorous energy estimates,” Institute of Science and Technology Austria, 2022.' ista: 'Mysliwy K. 2022. Polarons in Bose gases and polar crystals: Some rigorous energy estimates. Institute of Science and Technology Austria.' mla: 'Mysliwy, Krzysztof. Polarons in Bose Gases and Polar Crystals: Some Rigorous Energy Estimates. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:11473.' short: 'K. Mysliwy, Polarons in Bose Gases and Polar Crystals: Some Rigorous Energy Estimates, Institute of Science and Technology Austria, 2022.' date_created: 2022-06-30T12:15:03Z date_published: 2022-07-01T00:00:00Z date_updated: 2023-09-07T13:43:52Z day: '01' ddc: - '515' - '539' degree_awarded: PhD department: - _id: GradSch - _id: RoSe doi: 10.15479/at:ista:11473 ec_funded: 1 file: - access_level: open_access checksum: 7970714a20a6052f75fb27a6c3e9976e content_type: application/pdf creator: kmysliwy date_created: 2022-07-05T08:12:56Z date_updated: 2022-07-05T08:12:56Z file_id: '11486' file_name: thes1_no_isbn_2_1b.pdf file_size: 1830973 relation: main_file success: 1 - access_level: closed checksum: 647a2011fdf56277096c9350fefe1097 content_type: application/zip creator: kmysliwy date_created: 2022-07-05T08:15:52Z date_updated: 2022-07-05T08:17:12Z file_id: '11487' file_name: thes_source.zip file_size: 5831060 relation: source_file file_date_updated: 2022-07-05T08:17:12Z has_accepted_license: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '138' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '10564' relation: part_of_dissertation status: public - id: '8705' relation: part_of_dissertation status: public status: public supervisor: - first_name: Robert full_name: Seiringer, Robert id: 4AFD0470-F248-11E8-B48F-1D18A9856A87 last_name: Seiringer orcid: 0000-0002-6781-0521 title: 'Polarons in Bose gases and polar crystals: Some rigorous energy estimates' type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2022' ... --- _id: '10799' abstract: - lang: eng text: "Because of the increasing popularity of machine learning methods, it is becoming important to understand the impact of learned components on automated decision-making systems and to guarantee that their consequences are beneficial to society. In other words, it is necessary to ensure that machine learning is sufficiently trustworthy to be used in real-world applications. This thesis studies two properties of machine learning models that are highly desirable for the\r\nsake of reliability: robustness and fairness. In the first part of the thesis we study the robustness of learning algorithms to training data corruption. Previous work has shown that machine learning models are vulnerable to a range\r\nof training set issues, varying from label noise through systematic biases to worst-case data manipulations. This is an especially relevant problem from a present perspective, since modern machine learning methods are particularly data hungry and therefore practitioners often have to rely on data collected from various external sources, e.g. from the Internet, from app users or via crowdsourcing. Naturally, such sources vary greatly in the quality and reliability of the\r\ndata they provide. With these considerations in mind, we study the problem of designing machine learning algorithms that are robust to corruptions in data coming from multiple sources. We show that, in contrast to the case of a single dataset with outliers, successful learning within this model is possible both theoretically and practically, even under worst-case data corruptions. The second part of this thesis deals with fairness-aware machine learning. There are multiple areas where machine learning models have shown promising results, but where careful considerations are required, in order to avoid discrimanative decisions taken by such learned components. Ensuring fairness can be particularly challenging, because real-world training datasets are expected to contain various forms of historical bias that may affect the learning process. In this thesis we show that data corruption can indeed render the problem of achieving fairness impossible, by tightly characterizing the theoretical limits of fair learning under worst-case data manipulations. However, assuming access to clean data, we also show how fairness-aware learning can be made practical in contexts beyond binary classification, in particular in the challenging learning to rank setting." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov citation: ama: Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:10.15479/at:ista:10799 apa: Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799 chicago: Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10799. ieee: N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022. ista: Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute of Science and Technology Austria. mla: Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799. short: N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute of Science and Technology Austria, 2022. date_created: 2022-02-28T13:03:49Z date_published: 2022-03-08T00:00:00Z date_updated: 2023-10-17T12:31:54Z day: '08' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: ChLa doi: 10.15479/at:ista:10799 ec_funded: 1 file: - access_level: open_access checksum: 626bc523ae8822d20e635d0e2d95182e content_type: application/pdf creator: nkonstan date_created: 2022-03-06T11:42:54Z date_updated: 2022-03-06T11:42:54Z file_id: '10823' file_name: thesis.pdf file_size: 4204905 relation: main_file success: 1 - access_level: closed checksum: e2ca2b88350ac8ea1515b948885cbcb1 content_type: application/x-zip-compressed creator: nkonstan date_created: 2022-03-06T11:42:57Z date_updated: 2022-03-10T12:11:48Z file_id: '10824' file_name: thesis.zip file_size: 22841103 relation: source_file file_date_updated: 2022-03-10T12:11:48Z has_accepted_license: '1' keyword: - robustness - fairness - machine learning - PAC learning - adversarial learning language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: '176' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication_identifier: isbn: - 978-3-99078-015-2 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '8724' relation: part_of_dissertation status: public - id: '10803' relation: part_of_dissertation status: public - id: '10802' relation: part_of_dissertation status: public - id: '6590' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Robustness and fairness in machine learning type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2022' ... --- _id: '11626' abstract: - lang: eng text: Plant growth and development is well known to be both, flexible and dynamic. The high capacity for post-embryonic organ formation and tissue regeneration requires tightly regulated intercellular communication and coordinated tissue polarization. One of the most important drivers for patterning and polarity in plant development is the phytohormone auxin. Auxin has the unique characteristic to establish polarized channels for its own active directional cell to cell transport. This fascinating phenomenon is called auxin canalization. Those auxin transport channels are characterized by the expression and polar, subcellular localization of PIN auxin efflux carriers. PIN proteins have the ability to dynamically change their localization and auxin itself can affect this by interfering with trafficking. Most of the underlying molecular mechanisms of canalization still remain enigmatic. What is known so far is that canonical auxin signaling is indispensable but also other non-canonical signaling components are thought to play a role. In order to shed light into the mysteries auf auxin canalization this study revisits the branches of auxin signaling in detail. Further a new auxin analogue, PISA, is developed which triggers auxin-like responses but does not directly activate canonical transcriptional auxin signaling. We revisit the direct auxin effect on PIN trafficking where we found that, contradictory to previous observations, auxin is very specifically promoting endocytosis of PIN2 but has no overall effect on endocytosis. Further, we evaluate which cellular processes related to PIN subcellular dynamics are involved in the establishment of auxin conducting channels and the formation of vascular tissue. We are re-evaluating the function of AUXIN BINDING PROTEIN 1 (ABP1) and provide a comprehensive picture about its developmental phneotypes and involvement in auxin signaling and canalization. Lastly, we are focusing on the crosstalk between the hormone strigolactone (SL) and auxin and found that SL is interfering with essentially all processes involved in auxin canalization in a non-transcriptional manner. Lastly we identify a new way of SL perception and signaling which is emanating from mitochondria, is independent of canonical SL signaling and is modulating primary root growth. alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Michelle C full_name: Gallei, Michelle C id: 35A03822-F248-11E8-B48F-1D18A9856A87 last_name: Gallei orcid: 0000-0003-1286-7368 citation: ama: Gallei MC. Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana. 2022. doi:10.15479/at:ista:11626 apa: Gallei, M. C. (2022). Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11626 chicago: Gallei, Michelle C. “Auxin and Strigolactone Non-Canonical Signaling Regulating Development in Arabidopsis Thaliana.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11626. ieee: M. C. Gallei, “Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana,” Institute of Science and Technology Austria, 2022. ista: Gallei MC. 2022. Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana. Institute of Science and Technology Austria. mla: Gallei, Michelle C. Auxin and Strigolactone Non-Canonical Signaling Regulating Development in Arabidopsis Thaliana. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:11626. short: M.C. Gallei, Auxin and Strigolactone Non-Canonical Signaling Regulating Development in Arabidopsis Thaliana, Institute of Science and Technology Austria, 2022. date_created: 2022-07-20T11:21:53Z date_published: 2022-07-20T00:00:00Z date_updated: 2023-11-07T08:20:13Z day: '20' ddc: - '575' degree_awarded: PhD department: - _id: GradSch - _id: JiFr doi: 10.15479/at:ista:11626 ec_funded: 1 file: - access_level: open_access checksum: bd7ac35403cf5b4b2607287d2a104b3a content_type: application/pdf creator: mgallei date_created: 2022-07-25T09:08:47Z date_updated: 2022-07-25T09:08:47Z file_id: '11645' file_name: Thesis_Gallei.pdf file_size: 9730864 relation: main_file - access_level: closed checksum: a9e54fe5471ba25dc13c2150c1b8ccbb content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: mgallei date_created: 2022-07-25T09:09:09Z date_updated: 2022-07-25T09:39:58Z file_id: '11646' file_name: Thesis_Gallei_source.docx file_size: 19560720 relation: source_file - access_level: closed checksum: 3994f7f20058941b5bb8a16886b21e71 content_type: application/pdf creator: mgallei date_created: 2022-07-25T09:09:32Z date_updated: 2022-07-25T09:39:58Z description: This is the print version of the thesis including the full appendix file_id: '11647' file_name: Thesis_Gallei_to_print.pdf file_size: 24542837 relation: source_file - access_level: open_access checksum: f24acd3c0d864f4c6676e8b0d7bfa76b content_type: application/pdf creator: mgallei date_created: 2022-07-25T11:48:45Z date_updated: 2022-07-25T11:48:45Z file_id: '11650' file_name: Thesis_Gallei_Appendix.pdf file_size: 15435966 relation: main_file file_date_updated: 2022-07-25T11:48:45Z has_accepted_license: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '248' project: - _id: 261099A6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '742985' name: Tracing Evolution of Auxin Transport and Polarity in Plants publication_identifier: isbn: - 978-3-99078-019-0 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '8931' relation: part_of_dissertation status: public - id: '9287' relation: part_of_dissertation status: public - id: '7142' relation: part_of_dissertation status: public - id: '7465' relation: part_of_dissertation status: public - id: '8138' relation: part_of_dissertation status: public - id: '6260' relation: part_of_dissertation status: public - id: '10411' relation: part_of_dissertation status: public status: public supervisor: - first_name: Jiří full_name: Friml, Jiří id: 4159519E-F248-11E8-B48F-1D18A9856A87 last_name: Friml orcid: 0000-0002-8302-7596 - first_name: Eva full_name: Benková, Eva id: 38F4F166-F248-11E8-B48F-1D18A9856A87 last_name: Benková orcid: 0000-0002-8510-9739 - first_name: Eilon full_name: Shani, Eilon last_name: Shani title: Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '12358' abstract: - lang: eng text: "The complex yarn structure of knitted and woven fabrics gives rise to both a mechanical and\r\nvisual complexity. The small-scale interactions of yarns colliding with and pulling on each\r\nother result in drastically different large-scale stretching and bending behavior, introducing\r\nanisotropy, curling, and more. While simulating cloth as individual yarns can reproduce this\r\ncomplexity and match the quality of real fabric, it may be too computationally expensive for\r\nlarge fabrics. On the other hand, continuum-based approaches do not need to discretize the\r\ncloth at a stitch-level, but it is non-trivial to find a material model that would replicate the\r\nlarge-scale behavior of yarn fabrics, and they discard the intricate visual detail. In this thesis,\r\nwe discuss three methods to try and bridge the gap between small-scale and large-scale yarn\r\nmechanics using numerical homogenization: fitting a continuum model to periodic yarn simulations, adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively\r\nfitting yarn parameters to physical measurements of real fabric.\r\nTo start, we present a method for animating yarn-level cloth effects using a thin-shell solver.\r\nWe first use a large number of periodic yarn-level simulations to build a model of the potential\r\nenergy density of the cloth, and then use it to compute forces in a thin-shell simulator. The\r\nresulting simulations faithfully reproduce expected effects like the stiffening of woven fabrics\r\nand the highly deformable nature and anisotropy of knitted fabrics at a fraction of the cost of\r\nfull yarn-level simulation.\r\nWhile our thin-shell simulations are able to capture large-scale yarn mechanics, they lack\r\nthe rich visual detail of yarn-level simulations. Therefore, we propose a method to animate\r\nyarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware\r\nfashion in real time. Using triangle strains to interpolate precomputed yarn geometry, we are\r\nable to reproduce effects such as knit loops tightening under stretching at negligible cost.\r\nFinally, we introduce a methodology for inverse-modeling of yarn-level mechanics of cloth,\r\nbased on the mechanical response of fabrics in the real world. We compile a database from\r\nphysical tests of several knitted fabrics used in the textile industry spanning diverse physical\r\nproperties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation, using homogenized\r\nshell models to speed up computation and adding some small-but-necessary extensions to\r\nyarn-level models used in computer graphics.\r\n" acknowledged_ssus: - _id: SSU alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Georg full_name: Sperl, Georg id: 4DD40360-F248-11E8-B48F-1D18A9856A87 last_name: Sperl citation: ama: 'Sperl G. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. 2022. doi:10.15479/at:ista:12103' apa: 'Sperl, G. (2022). Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12103' chicago: 'Sperl, Georg. “Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:12103.' ieee: 'G. Sperl, “Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting,” Institute of Science and Technology Austria, 2022.' ista: 'Sperl G. 2022. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria.' mla: 'Sperl, Georg. Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:12103.' short: 'G. Sperl, Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting, Institute of Science and Technology Austria, 2022.' date_created: 2023-01-24T10:49:46Z date_published: 2022-09-22T00:00:00Z date_updated: 2024-02-28T12:57:46Z day: '22' ddc: - '000' - '620' degree_awarded: PhD department: - _id: GradSch - _id: ChWo doi: 10.15479/at:ista:12103 ec_funded: 1 file: - access_level: open_access checksum: 083722acbb8115e52e3b0fdec6226769 content_type: application/pdf creator: cchlebak date_created: 2023-01-25T12:04:41Z date_updated: 2023-02-02T09:29:57Z description: 'This is the main PDF file of the thesis. File size: 105 MB' file_id: '12371' file_name: thesis_gsperl.pdf file_size: 104497530 relation: main_file title: Thesis - access_level: open_access checksum: 511f82025e5fcb70bff4731d6896ca07 content_type: application/pdf creator: cchlebak date_created: 2023-02-02T09:33:37Z date_updated: 2023-02-02T09:33:37Z description: This version of the thesis uses stronger image compression for a smaller file size of 23MB. file_id: '12483' file_name: thesis_gsperl_compressed.pdf file_size: 23183710 relation: main_file title: Thesis (compressed 23MB) - access_level: open_access checksum: ed4cb85225eedff761c25bddfc37a2ed content_type: application/x-zip-compressed creator: cchlebak date_created: 2023-02-02T09:39:25Z date_updated: 2023-02-02T09:39:25Z file_id: '12484' file_name: thesis-source.zip file_size: 98382247 relation: source_file file_date_updated: 2023-02-02T09:39:25Z has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '138' project: - _id: 2533E772-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '638176' name: Efficient Simulation of Natural Phenomena at Extremely Large Scales publication_identifier: isbn: - 978-3-99078-020-6 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '11736' relation: part_of_dissertation status: public - id: '9818' relation: part_of_dissertation status: public - id: '8385' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 title: 'Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting' type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '10759' 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. alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 citation: ama: Rzadkowski W. Analytic and machine learning approaches to composite quantum impurities. 2022. doi:10.15479/at:ista:10759 apa: Rzadkowski, W. (2022). Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10759 chicago: Rzadkowski, Wojciech. “Analytic and Machine Learning Approaches to Composite Quantum Impurities.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10759. ieee: W. Rzadkowski, “Analytic and machine learning approaches to composite quantum impurities,” Institute of Science and Technology Austria, 2022. ista: Rzadkowski W. 2022. Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria. mla: Rzadkowski, Wojciech. Analytic and Machine Learning Approaches to Composite Quantum Impurities. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10759. short: W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum Impurities, Institute of Science and Technology Austria, 2022. date_created: 2022-02-16T13:27:37Z date_published: 2022-02-21T00:00:00Z date_updated: 2024-02-28T13:01:59Z day: '21' ddc: - '530' degree_awarded: PhD department: - _id: GradSch - _id: MiLe doi: 10.15479/at:ista:10759 ec_funded: 1 file: - access_level: closed checksum: 0fc54ad1eaede879c665ac9b53c93e22 content_type: application/zip creator: wrzadkow date_created: 2022-02-21T13:58:16Z date_updated: 2022-02-22T07:20:12Z file_id: '10785' file_name: Rzadkowski_thesis_final_source.zip file_size: 17668233 relation: source_file - access_level: open_access checksum: 22d2d7af37ca31f6b1730c26cac7bced content_type: application/pdf creator: wrzadkow date_created: 2022-02-21T14:02:54Z date_updated: 2022-02-21T14:02:54Z file_id: '10786' file_name: Rzadkowski_thesis_final.pdf file_size: 13307331 relation: main_file success: 1 file_date_updated: 2022-02-22T07:20:12Z has_accepted_license: '1' language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: '120' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '10762' relation: part_of_dissertation status: public - id: '8644' relation: part_of_dissertation status: public - id: '7956' relation: part_of_dissertation status: public - id: '415' relation: part_of_dissertation status: public status: public supervisor: - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 title: Analytic and machine learning approaches to composite quantum impurities type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2022' ...