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Large strain constitutive modelling of soft compressible and incompressible solids: Generalised isotropic and anisotropic viscoelasticity

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Journal of the Mechanics and Physics of Solids 2025年 203卷

作者： Zeng Liu Rogelio Ortigosa Antonio J. Gil Javier Bonet Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE) Barcelona Spain Departament de Enginyeria Civil i Ambiental (DECA) Universitat Politècnica de Catalunya Barcelona Spain Computational Mechanics and Scientific Computing Group Technical University of Cartagena Campus Muralla del Mar 30202 Cartagena (Murcia) Spain Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Bay Campus SA1 8EN United Kingdom

This paper discusses a new phenomenological continuum formulation for the constitutive modelling of viscoelastic materials at large strains. Following pioneering works in Sidoroff (1974), Lubliner (1985), Bergström (1998) and Reese and Govindjee (1997), the formulation shares some common ingredients with other phenomenological approaches, including the multiplicative decomposition of the deformation gradient into viscous and elastic contributions, the additive Maxwell-type decomposition of the strain energy density, and the definition of a set of kinematic internal state variables with their associated evolution laws. Our formulation departs from other state-of-the-art methodologies via three distinct novelties. First, and revisiting previous work by Bonet (2001), the paper introduces a thermodynamically consistent linear rate type evolution law in terms of stress-type variables, which resembles the return mapping algorithm typically used in elastoplasticity, facilitating the modelling link between both inelastic constitutive models. In this sense, the proposed viscoelastic evolution law can be identified with a classical plastic flow rule. Very importantly, the evolution law is shown to be compatible with the second law of thermodynamics by construction and have a closed-form solution in the case of incompressible viscoelasticity when using a prototypical neo-Hookean type of non-equilibrium strain energy density. Moreover, the paper shows how using the concept of a stress-driven dissipative potential, more general non-linear type of stress evolution laws can be straightforwardly constructed. Second, to facilitate the joint consideration of anisotropy and thermodynamic equilibrium, a frame indifferent stress free configuration is introduced which facilitates the definition of objective strain measures. Third, the methodology is extended from isotropy to transverse isotropy via the consideration of the appropriate structural tensor. The formulation is first displayed

关键词： Large strain Finite viscoelasticity Maxwell rheological model Multiplicative decomposition

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Discovery of Magnetospheric Interactions in the Doubly-Magnetic Hot Binary ∊ Lupi

arXiv

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arXiv 2023年

作者： Biswas, Ayan Das, Barnali Chandra, Poonam Wade, Gregg A. Shultz, Matthew E. Cavallaro, Francesco Petit, Veronique Woudt, Patrick A. Alecian, Evelyne National Centre for Radio Astrophysics Tata Institute of Fundamental Research Ganeshkhind Pune411007 India Department of Physics Engineering Physics & Astronomy Queen's University KingstonONK7L 3N6 Canada Department of Physics & Space Science Royal Military College of Canada PO Box 17000 Station Forces KingstonONK7K 0C6 Canada Department of Physics and Astronomy University of Delaware NewarkDE19716 United States National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903 United States Inter-University Institute for Data Intensive Astronomy Department of Astronomy University of Cape Town Private Bag X3 Rondebosch7701 South Africa Univ. Grenoble Alpes CNRS IPAG Grenoble38000 France

Magnetic fields are extremely rare in close, hot binaries, with only 1.5% of such systems known to contain a magnetic star. The eccentric ∊ Lupi system stands out in this population as the only close binary in which both stars are known to be magnetic. We report the discovery of strong, variable radio emission from ∊ Lupi using the upgraded Giant Metrewave Radio Telescope (uGMRT) and the MeerKAT radio telescope. The light curve exhibits striking, unique characteristics including sharp, high-amplitude pulses that repeat with the orbital period, with the brightest enhancement occurring near periastron. The characteristics of the light curve point to variable levels of magnetic reconnection throughout the orbital cycle, making ∊ Lupi the first known high-mass, main sequence binary embedded in an interacting magnetosphere. We also present a previously unreported enhancement in the X-ray light curve obtained from archival XMM-Newton data. The stability of the components' fossil magnetic fields, the firm characterization of their relatively simple configurations, and the short orbital period of the system make ∊ Lupi an ideal target to study the physics of magnetospheric interactions. This system may thus help us to illuminate the exotic plasma physics of other magnetically interacting systems such as moon-planet, planet-star, and star-star systems including T Tauri binaries, RS CVn systems, and neutron star binaries. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic fields

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Constructions of batch codes via finite geometry

arXiv

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arXiv 2019年

作者： Polyanskii, Nikita Vorobyev, Ilya Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow127051 Russia Advanced Combinatorics and Complex Networks Lab Moscow Institute of Physics and Technology Dolgoprudny141701 Russia

A primitive k-batch code encodes a string x of length n into string y of length N, such that each multiset of k symbols from x has k mutually disjoint recovering sets from y. We develop new explicit and random coding constructions of linear primitive batch codes based on finite geometry. In some parameter regimes, our proposed codes have lower redundancy than previously known batch codes. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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A New Algorithm for Two-Stage Group Testing

arXiv

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arXiv 2019年

作者： Vorobyev, Ilya Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow127051 Russia Advanced Combinatorics and Complex Networks Lab Moscow Institute of Physics and Technology Dolgoprudny141701 Russia

Group testing is a well-known search problem that consists in detecting of s defective members of a set of t samples by carrying out tests on properly chosen subsets of samples. In classical group testing the goal is to find all defective elements by using the minimal possible number of tests in the worst case. In this work, two-stage group testing is considered. Using the hypergraph approach we design a new search algorithm, which allows improving the known results for fixed s and t → ∞. For the case s = 2 this algorithm achieves information-theoretic lower bound 2 log2 t(1 + o(1)) on the number of tests in the worst case. Also, the problem of finding m out of s defectives is considered. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Empirical study of extreme overfitting points of neural networks

arXiv

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arXiv 2019年

作者： Merkulov, Daniil Oseledets, Ivan Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Moscow Russia Moscow Institute of Physics and Technology Moscow Russia Institute of Numerical Mathematics Russian Academy of Sciences Moscow Russia

In this paper we propose a method of obtaining points of extreme overfitting - parameters of modern neural networks, at which they demonstrate close to 100 % training accuracy, simultaneously with almost zero accuracy on the test sample. Despite the widespread opinion that the overwhelming majority of critical points of the loss function of a neural network have equally good generalizing ability, such points have a huge generalization error. The paper studies the properties of such points and their location on the surface of the loss function of modern neural networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Evaluating deep transfer learning for whole-brain cognitive decoding

arXiv

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arXiv 2021年

作者： Thomas, Armin W. Lindenberger, Ulman Samek, Wojciech Müller, Klaus-Robert Machine Learning Group Dept. of Computer Science and Electrical Engineering Technische Universität Berlin Berlin Germany Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany Stanford Data Science Stanford University StanfordCA United States Dept. of Psychology Stanford University StanfordCA United States Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Dept. of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states. © 2021, CC BY.

关键词： Decoding

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Time- and memory-efficient representation of complex mesoscale potentials

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JOURNAL OF computational PHYSICS 2017年 343卷 110-114页

作者： Drozdov, Grigory Ostanin, Igor Oseledets, Ivan Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

We apply the modern technique of approximation of multivariate functions - tensor train cross approximation - to the problem of the description of physical interactions between complex-shaped bodies in a context of computational nanomechanics. In this note we showcase one particular example - van der Waals interactions between two cylindrical bodies - relevant to modeling of carbon nanotube systems. The potential is viewed as a tensor ( multidimensional table) which is represented in compact form with the help of tensor train decomposition. The described approach offers a universal solution for the description of van der Waals interactions between complex-shaped nanostructures and can be used within the framework of such systems of mesoscale modeling as recently emerged mesoscopic distinct element method (MDEM). (C) 2017 Elsevier Inc. All rights reserved.

关键词： Mesoscale modeling Tensor train cross approximation

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Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records

arXiv

引用

arXiv 2021年

作者： Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S.O. Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Centre for Quantitative Medicine and Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Faculty of Medicine Nursing and Health Sciences Monash University VIC Australia Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Division of Pulmonary Allergy and Critical Care Medicine Duke University DurhamNC United States Department of Emergency Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. © 2021, CC BY.

关键词： Learning systems

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Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report

arXiv

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arXiv 2024年

作者： Huijben, Evi M.C. Terpstra, Maarten L. Galapon, Arthur Pai, Suraj Thummerer, Adrian Koopmans, Peter Afonso, Manya van Eijnatten, Maureen Gurney-Champion, Oliver Chen, Zeli Zhang, Yiwen Zheng, Kaiyi Li, Chuanpu Pang, Haowen Ye, Chuyang Wang, Runqi Song, Tao Fan, Fuxin Qiu, Jingna Huang, Yixing Ha, Juhyung Park, Jong Sung Alain-Beaudoin, Alexandra Bériault, Silvain Yu, Pengxin Guo, Hongbin Huang, Zhanyao Li, Gengwan Zhang, Xueru Fan, Yubo Liu, Han Xin, Bowen Nicolson, Aaron Zhong, Lujia Deng, Zhiwei Müller-Franzes, Gustav Khader, Firas Li, Xia Zhang, Ye Hémon, Cédric Boussot, Valentin Zhang, Zhihao Wang, Long Bai, Lu Wang, Shaobin Mus, Derk Kooiman, Bram Sargeant, Chelsea A.H. Henderson, Edward G.A. Kondo, Satoshi Kasai, Satoshi Karimzadeh, Reza Ibragimov, Bulat Helfer, Thomas Dafflon, Jessica Chen, Zijie Wang, Enpei Perko, Zoltan Maspero, Matteo Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Radiotherapy Department University Medical Center Utrecht Utrecht Netherlands Computational Imaging Group for MR Diagnostics & Therapy University Medical Center Utrecht Utrecht Netherlands Department of Radiation Oncology University Medical Center Groningen University of Groningen Groningen Netherlands GROW School for Oncology Maastricht University Medical Centre Maastricht Netherlands Department of Radiation Oncology LMU University Hospital LMU Munich Munich Germany Department of Radiation Oncology Radboud University Medical Center Nijmegen Netherlands Wageningen University & Research Wageningen Plant Research Wageningen Netherlands Department of Radiology and Nuclear Medicine Amsterdam UMC Location University of Amsterdam Amsterdam Netherlands Cancer Center Amsterdam Imaging and Biomarkers Amsterdam Netherlands School of Biomedical Engineering Southern Medical University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China School of Biomedical Engineering ShanghaiTech University Shanghai China Fudan University Shanghai China Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Indiana University Bloomington United States Advanced Development Engineering Elekta Ltd Montreal Canada Infervision Medical Technology Co. Ltd. Beijing China Department of Biomedical Engineering Shantou University China Independent researchers Department of Computer Science Vanderbilt University Nashville United States Australian e-Health Research Centre CSIRO HerstonQLD Australia Los AngelesCA United States University Hospital Aachen Aachen Germany Center for Proton Therapy Paul Scherrer Institut Villigen Switzerland Department of Computer Science ETH Zurich Zurich Switzerland University Rennes 1 CLCC Eugène Marquis INSERM LTSI Rennes France Subtle Medical Shanghai China MedMind Technology Co.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥ 0.87/0.90) and gamma pass rates for photon (≥ 98.1%/99.0%) and proton (≥ 97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning. © 2024, CC

关键词： Radiotherapy

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Iterative joint image demosaicking and denoising using a residual denoising network

arXiv

引用

arXiv 2018年

作者： Kokkinos, Filippos Lefkimmiatis, Stamatios Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology

Modern digital cameras rely on the sequential execution of separate image processing steps to produce realistic images. The first two steps are usually related to denoising and demosaicking where the former aims to reduce noise from the sensor and the latter converts a series of light intensity readings to color images. Modern approaches try to jointly solve these problems, i.e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise. While there are several machine learning systems that have been recently introduced to solve this problem, the majority of them relies on generic network architectures which do not explicitly take into account the physical image model. In this work we propose a novel algorithm which is inspired by powerful classical image regularization methods, large-scale optimization, and deep learning techniques. Consequently, our derived iterative optimization algorithm, which involves a trainable denoising network, has a transparent and clear interpretation compared to other black-box data driven approaches. Our extensive experimentation line demonstrates that our proposed method outperforms any previous approaches for both noisy and noisefree data across many different datasets. This improvement in reconstruction quality is attributed to the rigorous derivation of an iterative solution and the principled way we design our denoising network architecture, which as a result requires fewer trainable parameters than the current state-of-the-art solution and furthermore can be efficiently trained by using a significantly smaller number of training data than existing deep demosaicking networks. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

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