{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2168c06dafca05699c31c2f2f66f4a97f/timricken", "tags" : [ "ANN","myown","quantum_computing" ], "intraHash" : "168c06dafca05699c31c2f2f66f4a97f", "interHash" : "e431f2583693313af435e39c2a95a518", "label" : "Evaluating Artificial Neural Networks and Quantum Computing for Mechanics", "user" : "timricken", "description" : "", "date" : "2020-02-18 20:56:08", "changeDate" : "2020-02-18 19:56:08", "count" : 3, "pub-type": "article", "journal": "PAMM", "year": "2019", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.201900470", "author": [ "André Mielke","Tim Ricken" ], "authors": [ {"first" : "André", "last" : "Mielke"}, {"first" : "Tim", "last" : "Ricken"} ], "volume": "19","number": "1","pages": "e201900470","abstract": "Abstract The popularization of Machine Learning (ML) and the advent of Noisy Intermediate-Scale Quantum (NISQ) devices for Quantum Computing (QC) sparked new inspiration for the search for techniques reducing computation time in mechanics. We evaluate artificial neural networks (ANNs) as candidates for creating computationally fast surrogate models for otherwise time-consuming simulations, using a multiscale and multiphase model describing processes in the human liver. We also give a short overview of interesting quantum-enhanced algorithms capable of reducing computational cost in parts of complex simulations.", "eprint" : "https://onlinelibrary.wiley.com/doi/pdf/10.1002/pamm.201900470", "doi" : "10.1002/pamm.201900470", "bibtexKey": "doi:10.1002/pamm.201900470" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/246934b35190d88a32720fea89389af71/timricken", "tags" : [ "ANN","myown","quantum_computing" ], "intraHash" : "46934b35190d88a32720fea89389af71", "interHash" : "94be78d3cc0f15eaaf066aff15b7838b", "label" : "Evaluating Artificial Neural Networks and Quantum Computing for Solving Mechanical Boundary Value Problems", "user" : "timricken", "description" : "", "date" : "2020-02-18 20:52:35", "changeDate" : "2020-02-18 19:52:35", "count" : 4, "pub-type": "inproceedings", "booktitle": "Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications","publisher":"CRC Press-Balkema","address":"PO box 11320, Leiden, 2301 EH, Netherlands", "year": "2019", "url": "", "author": [ "A. Mielke","T. Ricken" ], "authors": [ {"first" : "A.", "last" : "Mielke"}, {"first" : "T.", "last" : "Ricken"} ], "editor": [ " Zingoni, A" ], "editors": [ {"first" : "", "last" : "Zingoni, A"} ], "pages": "537-542","note": "7th International Conference on Structural Engineering, Mechanics and Computation (SEMC), Cape Town, SOUTH AFRICA, SEP 02-04, 2019","abstract": "Artificial neural networks (ANNs) and quantum computing (QC) have both received rapidly increasing interest in recent years for several reasons. Their roles in the mechanics community remain a subject of ongoing debate and research. ANNs have successfully been employed on problems previously considered unsolvable (Silver et al. 2016), as well as on practical and illustrative problems especially in the image recognition domain (Szegedy et al. 2013, among others). The most straightforward way to interpret the behavior of ANNs is focusing on their ability to approximate any continuous function to arbitrary precision (Kreinovich 1991). The adjustment to a given function happens automatically except for hyperparameters, such as the number of hidden layers or the learning rate, which remain constant during training. This makes them a perfect tool for reducing computational cost in otherwise time-consuming simulations.\r\nQuantum computing on the other hand has received some publicity in 2018 mainly because of a paper published by Google Quantum A.I. Lab scientists defining quantum supremacy (Boixo & Isakov 2018), where it is shown that a fully working universal quantum computer with only 50 qubits will outperform any near-term realizable classical supercomputer on certain tasks. This publicationwas followed up by Google revealing Bristlecone (Kelly 2018), a quantum processor consisting of 72 qubits. \r\nWhile many interesting quantum algorithms have proven to run polynomially or even exponentially faster than their classical analogons (Montanaro 2016), these results only hold true for a decoherence-free machine. Therefore, to actually achieve quantum supremacy, quantum error correction schemes need to be implemented, requiring several physical qubits to make up a single logical qubit suitable for computation.", "isbn" : "978-0-429-42650-6; 978-1-138-38696-9", "language" : "English", "affiliation" : "Mielke, A (Reprint Author), Univ Stuttgart, Inst Mech Struct Anal & Dynam ISD, Stuttgart, Germany. Mielke, A.; Ricken, T., Univ Stuttgart, Inst Mech Struct Anal & Dynam ISD, Stuttgart, Germany.", "usage-count-last-180-days" : "0", "web-of-science-categories" : "Engineering, Civil; Materials Science, Multidisciplinary", "number-of-cited-references" : "26", "research-areas" : "Engineering; Materials Science", "times-cited" : "0", "usage-count-since-2013" : "0", "doc-delivery-number" : "BO2AY", "da" : "2020-01-17", "unique-id" : "ISI:000504648100094", "bibtexKey": "ISI:000504648100094" } ] }