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Search for new physics in the τ lepton plus missing transverse momentum final state in proton-proton collisions at √s = 13 TeV

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Journal of High Energy Physics 2023年第9期2023卷 51页

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Rejeb Sfar, H. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Makarenko, I. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Taliercio, A. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Thakur, S. Cheng, T. Javaid, T. Mittal, M. Yuan, L. Ahmad, M. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Milosevic, V. Monti, F. Sharma, R. Tao, J. Thomas-Wilsker Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

A search for physics beyond the standard model (SM) in the final state with a hadronically decaying tau lepton and a neutrino is presented. This analysis is based on data recorded by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV at the LHC, corresponding to a total integrated luminosity of 138 fb⁻¹. The transverse mass spectrum is analyzed for the presence of new physics. No significant deviation from the SM prediction is observed. Limits are set on the production cross section of a W′ boson decaying into a tau lepton and a neutrino. Lower limits are set on the mass of the sequential SM-like heavy charged vector boson and the mass of a quantum black hole. Upper limits are placed on the couplings of a new boson to the SM fermions. Constraints are put on a nonuniversal gauge interaction model and an effective field theory model. For the first time, upper limits on the cross section of t-channel leptoquark (LQ) exchange are presented. These limits are translated into exclusion limits on the LQ mass and on its coupling in the t-channel. The sensitivity of this analysis extends into the parameter space of LQ models that attempt to explain the anomalies observed in B meson decays. The limits presented for the various interpretations are the most stringent to date. Additionally, a model-independent limit is provided. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Beyond Standard Model Hadron-Hadron Scattering Tau Physics

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Hardy inequalities for inverse square potentials with countable number of singularities

arXiv

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arXiv 2019年

作者： Cazacu, Cristian Marica, Aurora Mihaela University of Bucharest 14 Academiei Street Bucharest010014 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics Romanian Academy No.13 Calea 13 Septembrie Sector 5 Bucharest050711 Romania Department of Mathematics and Computer Sciences Faculty of Applied Sciences University Politehnica of Bucharest 313 Splaiul Independentei Bucharest060042 Romania University of Bucharest 90-92 Sos. Panduri 5th District Bucharest Romania

The Hardy Inequality (HI) for potentials with countably many singularities of the form V = Σkϵ1/|x-ak|2is not a trivial issue. In principle, the more singular poles are, the less the Hardy constant is: it is well-known that in all the existing results about the HI with finite number of singularities the best constants converge to 0 with the number n of singularities going to infinity. In this note we provide an example of nontrivial HI in right cylinders of fixed radius R > 0 in d, for a potential V defined above having the singularities {ak}kϵuniformly distributed on the axis of the cylinders. For this example we prove that an upper bound for the Hardy constant is (d - 2)2/4, the clasical Hardy constant in dcorresponding to one singular potential. We also prove positive lower bounds of the Hardy constant which allow to deduce that the asymptotic behavior as R → 0 of the Hardy constant coincides with (d - 2)2/4. The proof of the main result lies on using a nice identity due to Allegretto and Huang [1, Thms. 1.1, 2.1] for particularly well chosen test functions. Copyright © 2019, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Identification of Néel vector orientation in antiferromagnetic domains switched by currents in NiO/Pt thin films

arXiv

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arXiv 2020年

作者： Schmitt, Christin Baldrati, Lorenzo Sanchez-Tejerina, Luis Schreiber, Felix Ross, Andrew Filianina, Mariia Ding, Shilei Fuhrmann, Felix Ramos, Rafael Maccherozzi, Francesco Backes, Dirk Mawass, Mohamad A. Kronast, Florian Valencia, Sergio Saitoh, Eiji Finocchio, Giovanni Kläui, Mathias Institute of Physics Johannes Gutenberg-University Mainz Mainz55128 Germany Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Department of Biomedical Dental Morphological and Functional Imaging Sciences University of Messina Messina98125 Italy Graduate School of Excellence Materials Science in Mainz Mainz55128 Germany State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China WPI-Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Diamond Light Source Chilton Didcot OxfordshireOX11 0DE United Kingdom Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Strasse 15 BerlinD-12489 Germany Institute for Materials Research Tohoku University Sendai980-8577 Japan The Institute of AI and Beyond The University of Tokyo Tokyo113-8656 Japan Center for Spintronics Research Network Tohoku University Sendai980-8577 Japan Department of Applied Physics The University of Tokyo Tokyo113-8656 Japan

Understanding the electrical manipulation of the antiferromagnetic order is a crucial aspect to enable the design of antiferromagnetic devices working at THz frequency. Focusing on collinear insulating antiferromagnetic NiO/Pt thin films as a materials platform, we identify the crystallographic orientation of the domains that can be switched by currents and quantify the Néel vector direction changes. We demonstrate electrical switching between different T-domains by current pulses, finding that the Néel vector orientation in these domains is along [±5 ±5 19], different compared to the bulk directions. The final state of the in-plane component of the Néel vector nip after switching by current pulses j along the [1 ± 1 0] directions is nip j. By comparing the observed Néel vector orientation and the strain in the thin films, assuming that this variation arises solely from magnetoelastic effects, we quantify the order of magnitude of the magnetoelastic coupling coefficient as b0 + 2b1 = 3 × 107 J/m3. This information is key for the understanding of current-induced switching in antiferromagnets and for the design and use of such devices as active elements in spintronic devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

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Physical Review B 2019年第7期99卷 075430-075430页

作者： Matthew E. Trusheim Noel H. Wan Kevin C. Chen Christopher J. Ciccarino Johannes Flick Ravishankar Sundararaman Girish Malladi Eric Bersin Michael Walsh Benjamin Lienhard Hassaram Bakhru Prineha Narang Dirk Englund Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA Department of Materials Science and Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA College of Nanoscale Science and Engineering State University of New York Polytechnic Institute 257 Fuller Road Albany New York 12203 USA

We report on quantum emission from Pb-related color centers in diamond following ion implantation and high-temperature vacuum annealing. First-principles calculations predict a negatively charged Pb-vacancy (PbV) center in a split-vacancy configuration, with a zero-phonon transition around 2.4 eV. Cryogenic photoluminescence measurements performed on emitters in nanofabricated pillars reveal several transitions, including a prominent doublet near 520 nm. The splitting of this doublet, 5.7 THz, exceeds that reported for other group-IV centers. These observations are consistent with the PbV center, which is expected to have a combination of narrow optical transitions and stable spin states, making it a promising system for quantum network nodes.

关键词： Atomic orbital Density functional theory Electronic structure of atoms & molecules Photoemission Quantum memories Quantum repeaters Single- and few-photon ionization & excitation

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The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

arXiv

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arXiv 2024年

作者： Correia de Verdier, Maria Saluja, Rachit Gagnon, Louis LaBella, Dominic Baid, Ujjwall Tahon, Nourel Hoda Foltyn-Dumitru, Martha Zhang, Jikai Alafif, Maram Baig, Saif Chang, Ken D’Anna, Gennaro Deptula, Lisa Gupta, Diviya Haider, Muhammad Ammar Hussain, Ali Iv, Michael Kontzialis, Marinos Manning, Paul Moodi, Farzan Nunes, Teresa Simon, Aaron Sollmann, Nico Vu, David Adewole, Maruf Albrecht, Jake Anazodo, Udunna Chai, Rongrong Chung, Verena Faghani, Shahriar Farahani, Keyvan Kazerooni, Anahita Fathi Iglesias, Eugenio Kofler, Florian Li, Hongwei Linguraru, Marius George Menze, Bjoern Moawad, Ahmed W. Velichko, Yury Wiestler, Benedikt Altes, Talissa Basavasagar, Patil Bendszus, Martin Brugnara, Gianluca Cho, Jaeyoung Dhemesh, Yaseen Fields, Brandon K.K. Garrett, Filip Gass, Jaime Hadjiiski, Lubomir Hattangadi-Gluth, Jona Hess, Christopher Houk, Jessica L. Isufi, Edvin Layfield, Lester J. Mastorakos, George Mongan, John Nedelec, Pierre Nguyen, Uyen Oliva, Sebastian Pease, Matthew W. Rastogi, Aditya Sinclair, Jason Smith, Robert X. Sugrue, Leo P. Thacker, Jonathan Vidic, Igor Villanueva-Meyer, Javier White, Nathan S. Aboian, Mariam Conte, Gian Marco Dale, Anders Sabuncu, Mert R. Seibert, Tyler M. Weinberg, Brent Abayazeed, Aly Huang, Raymond Turk, Sevcan Rauschecker, Andreas M. Farid, Nikdokht Vollmuth, Philipp Nada, Ayman Bakas, Spyridon Calabrese, Evan Rudie, Jeffrey D. Department of Surgical Sciences Section of Neuroradiology Uppsala University Sweden Department of Radiology University of California San DiegoCA United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine United States Department of Pathology and Laboratory Medicine Indiana Univeristy IN United States Department of Neuroradiology Heidelberg University Germany University of Laval Quebec CityQC Canada Department of Radiation Oncology Duke University DurhamNC United States Radiology Department University of Missouri ColumbiaMO United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Radiology Nassau Medical Center East MeadowNY United States Department of Radiology Stanford University CA United States Neuroimaging Unit ASST Ovest Milanese Legnano Milan Italy Ross University School of Medicine Bridgetown Barbados Department of Radiology University of California Los AngelesCA United States C.M.H. Lahore Medical College Institute of Dentistry Lahore Pakistan Department of Imaging Sciences University of Rochester Medical Center RochesterNY United States Department of Radiology Northwestern University ChicagoIL United States Department of Radiology VA San Diego Medical Center United States School of Medicine Iran University of Medical Sciences Tehran Iran Neuroradiology Department Hospital Garcia de Orta Unidade Local de Saúde de Almada Seixal EPE Almada Portugal Imaging Department Hospital da Luz Lisboa Lisbon Portugal Department of Radiation Oncology University of California IrvineCA United States Department of Diagnostic and Interventional Radiology University Hospital Ulm Ulm Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Munich Germany Department of Radiology Scripps Clin

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care. © 2024, CC BY.

关键词： Magnetic resonance imaging

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A Comparative Evaluation of SteamVR Tracking and the OptiTrack System for Medical Device Tracking

A Comparative Evaluation of SteamVR Tracking and the OptiTra...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tim Ameler Kai Blohme Lilith Brandt Raphael Brüngel Alice Hensel Lisa Huber Francis Kuper Jessica Swoboda Maren Warnecke Michaela Warzecha Daniel Heß Johannes Frömke Annette Schmitz-Stolbrink Christoph M. Friedrich Department of Computer Science University of Applied Sciences and Arts Dortmund (FH Dortmund) Dortmund Germany Institut für die Digitalisierung von Arbeits- und Lebenswelten (IDiAL) University of Applied Sciences and Arts Dortmund (FH Dortmund) Dortmund Germany Cardiac Thoracic Vascular Surgery St.-Johannes-Hospital Dortmund Dortmund Germany Pediatric Radiology Klinikum Dortmund Dortmund Germany Institute for Medical Informatics University Hospital Essen Essen Germany

Tracking of medical devices can be used in diverse situations, e.g., training as well as image guidance for surgery and surgery planning. Therefore, position and orientation of a device, for instance, an ultrasound probe, need to be identified as precisely as possible. This enables correct representation of digital 3D models in medical image processing platforms such as 3D Slicer or MevisLab. In this manuscript, a comparative evaluation of the low-cost Swept Angle Laser Tracking (SALT) system SteamVR Tracking and the multi-camera-based Opti-Track System is presented. Their potential for medical device tracking is demonstrated in the use case of ultrasound probe tracking for simulation purposes. An evaluation of tracking errors is performed using a Universal Robotics UR5 industrial robot under non-laboratory conditions, involving common issues such as reflections and occlusions. A discussion on the tracking accuracy of both systems is given. The communication of tracking data is established for 3D Slicer and MeVisLab with the use of the PLUS Toolkit via the OpenIGTLink protocol.

关键词： Ultrasonic imaging Servers Probes Three-dimensional displays Robots Solid modeling Software

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Preface of the 3rd international workshop on (Meta)Modelling for Healthcare Systems (MMHS 2018) 3

Preface of the 3rd international workshop on (Meta)Modelling...

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3rd International Workshop on (Meta)Modelling for Healthcare Systems, MMHS 2018

作者： Rutle, Adrian Lamo, Yngve MacCaull, Wendy Iovino, Ludovico Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Department of Mathematics Statistics and Computer Science St. Francis Xavier University Antigonish Canada Gran Sasso Science Institute V.le Francesco Crispi L’Aquila Italy

MMHS 2018 was arranged as a joint workshop with the HealthTech seminar at Western Norway University of applied sciences, Bergen, Norway, June 13-15. It was the third workshop in the MMHS workshop series focusing on modeling and metamodeling for healthcare systems. E-health is one of the fastest growing ICT markets. According to Mordor Intelligence the market share for e-health was estimated as USD 124 billion in 2016 and is projected to reach USD 244 billion by 2021, growing at a compound annual growth rate of 14.56% during the forecast period. Major software and ICT infrastructure companies are currently investing for this growth. Several modelling approaches have been successfully implemented in the healthcare domain, such as business process modelling, domain specific languages for representation of health resources (HL7 FHIR), and conceptual modelling with archetypes (OPEN EHR), However, there is still a huge potential for metamodelling and model based software engineering to tackle problems such as software interoperability, formalization and simulation of work processes and clinical guidelines, domain specific abstractions, etc. The goal of this workshop was to create a forum for healthcare system developers and researchers on the one side and modeling researchers and the modeling community on the other. © 2018 CEUR-WS. All rights reserved.

关键词： Interoperability

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Detection of Malfunctional Smart Electricity Meters Based on Deep Learning of Electricity Usage Data

arXiv

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arXiv 2019年

作者： Liu, Ming Liu, Dongpeng Sun, Guangyu Zhao, Yi Wang, Duolin Liu, Fangxing Fang, Xiang He, Qing Xu, Dong Key Laboratory for Applied Statistics of Moe and School of Mathematics and Statistics Northeast Normal University Changchun130024 China Department of Applied Mathematics School of Mathematics and Statistics Sciences Changchun University of Technology Changchun130012 China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States Co. Ltd Beijing100084 China National Institute of Metrology Beijing100029 China

Detecting malfunctional smart meters based on electricity usage and targeting them for replacement can save significant resources. For this purpose, we developed a novel deep-learning method for malfunctional smart meter detection based on long short-term memory (LSTM) and a modified convolutional neural network (CNN). Our method uses LSTM to predict the reading of a master meter based on data collected from submeters. If the predicted value is significantly different from master meter reading data over a period of time, the diagnosis part will be activated, classifying every submeter to identify the malfunctional submeter based on CNN. We propose a time series-recurrence plot (TS-RP) CNN, by combining the sequential raw data of electricity and its recurrence plots in the phase space as dual input branches of CNN. By combining this time sequential (TS) raw data with the recurrence plots (RP), we found that the classification performance was much better than when using the sequential raw data only. We compared our method with several classical methods, including the elastic net and gradient boosting regression methods, which show that our method performs better. To the best of our knowledge, our TS-RP CNN is the first method to apply deep learning in malfunctional meter detection. It is also relatively unique in the way it combines sequential data and its phase-space transformation as the dual input for general sequential data classification. This method is not only useful for increasing the service life span of smart meters, preventing unnecessary replacement, but it also provides a general method for managing other instruments of sequential data. Copyright © 2019, The Authors. All rights reserved.

关键词： Smart meters

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Author Correction: Multidimensional nanoscopic chiroptics

Nature Reviews Physics

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Nature Reviews Physics 2022年 138页

作者： Yang Chen Qing Zhang Cheng-Wei Qiu Wei Du Oscar Ávalos-Ovando Alexander O. Govorov Jing Wu Qing-Hua Xu Na Liu Hiromi Okamoto Qihua Xiong Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Institute of Functional Nano and Soft Materials (FUNSOM) Soochow University Suzhou China Department of Physics and Astronomy Ohio University Athens OH USA Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) Singapore Singapore Department of Chemistry National University of Singapore Singapore Singapore 2nd Physics Institute University of Stuttgart Stuttgart Germany Max Planck Institute for Solid State Research Stuttgart Germany Institute for Molecular Science and the Graduate University for Advanced Studies (SOKENDAI) Okazaki Aichi Japan State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing China Beijing Academy of Quantum Information Sciences (BAQIS) Beijing China

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A Bayesian estimation method for variational phase-field fracture problems

arXiv

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arXiv 2019年

作者： Khodadadian, Amirreza Noii, Nima Parvizi, Maryam Abbaszadeh, Mostafa Wick, Thomas Heitzinger, Clemens Wiedner Hauptstraße 8-10 Vienna1040 Austria Faculty of Mathematics and Computer Sciences Amirkabir University of Technology No. 424 Hafez Ave. 15914 Tehran Iran Institute of Applied Mathematics Leibniz University Hannover Welfengarten 1 Hanover30167 Germany School of Mathematical and Statistical Sciences Arizona State University TempeAZ85287 United States

In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian framework. Here, the focus is on uncertainties arising in the solid material parameters and the critical energy release rate. A reference value (obtained on a sufficiently small mesh) as the replacement of measurement will be chosen, and their posterior distribution is obtained. Due to time- and mesh dependency of the problem, the computational costs can be high. Using Bayesian inversion, we solve the problem on a relatively coarse mesh and fit the parameters. The obtained load-displacement curve that is usually the target function is matched with the reference values. Finally, our algorithmic approach is substantiated with several numerical examples. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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