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Discovering plasticity models without stress data

npj Computational Materials 2022年第1期8卷 857-866页

作者： Moritz Flaschel siddhant kumar Laura De Lorenzis Department of Mechanical and Process Engineering ETH Zürich8092ZürichSwitzerland Department of Materials Science and Engineering Delft University of Technology2628 CDDelftThe Netherlands

We propose an approach for data-driven automated discovery of material laws,which we call EUCLID(Efficient Unsupervised Constitutive Law Identification and Discovery),and we apply it here to the discovery of plasticity models,including arbitrarily shaped yield surfaces and isotropic and/or kinematic hardening *** approach is unsupervised,i.e.,it requires no stress data but only full-field displacement and global force data;it delivers interpretable models,i.e.,models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a potentially large catalog of candidate functions;it is one-shot,i.e.,discovery only needs one *** material model library is constructed by expanding the yield function with a Fourier series,whereas isotropic and kinematic hardening is introduced by assuming a yield function dependency on internal history variables that evolve with the plastic *** selecting the most relevant Fourier modes and identifying the hardening behavior,EUCLID employs physics knowledge,i.e.,the optimization problem that governs the discovery enforces the equilibrium constraints in the bulk and at the loaded boundary of the *** promoting regularization is deployed to generate a set of solutions out of which a solution with low cost and high parsimony is automatically *** virtual experiments,we demonstrate the ability of EUCLID to accurately discover several plastic yield surfaces and hardening mechanisms of different complexity.

关键词： stress plasticity selecting

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Inverse-designed spinodoid metamaterials

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npj Computational Materials 2020年第1期6卷 1029-1038页

作者： siddhant kumar Stephanie Tan Li Zheng Dennis M.Kochmann Mechanics&Materials Lab Department of Mechanical and Process EngineeringETH Zürich8092 ZürichSwitzerland Faculty of Electrical Engineering Mathematics and Computer ScienceDelft University of Technology2628 CD DelftNetherlands Graduate Aerospace Laboratories California Institute of TechnologyPasadenaCA 91125USA

After a decade of periodic truss-,plate-,and shell-based architectures having dominated the design of metamaterials,we introduce the non-periodic class of spinodoid *** by natural self-assembly processes,spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase *** theoretical parametrization is so intriguingly simple that one can bypass costly phase-field simulations and obtain a rich and seamlessly tunable property ***,breaking with the periodicity of classical metamaterials is the enabling factor to the large property space and the ability to introduce seamless functional *** introduce an efficient and robust machine learning technique for the inverse design of(meta-)materials which,when applied to spinodoid topologies,enables us to generate uniform and functionally graded cellular mechanical metamaterials with tailored direction-dependent(anisotropic)stiffness and *** specifically present biomimetic artificial bone architectures that not only reproduce the properties of trabecular bone accurately but also even geometrically resemble natural bone.

关键词： density property periodic

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Spatially-graded 3D-printed viscoelastic truss metamaterials for impact trajectory control and energy absorption

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Theoretical & Applied Mechanics Letters 2024年第5期14卷 378-385页

作者： Kaoutar Radi Raphaël N.Glaesener siddhant kumar Dennis M.Kochmann Mechanics&Materials Lab Department of Mechanical and Process EngineeringETH Zürich 8092Switzerland Mechanics&Materials Computing Lab Department of Materials Science&EngineeringTU Delft 2628 CNNetherlands

This study demonstrates that two-and three-dimensional spatially graded,truss-based polymeric-material metamaterials can be designed for beneficial impact mitigation and energy absorption *** a combination of numerical and experimental techniques,we highlight the broad property space of periodic viscoelastic trusses,realized using 3D printing via selective laser *** beyond periodic designs,we investigate the impact response of spatially variant viscoelastic lattices in both two and three *** result reveal that introducing spatial variations in lattice topology allows for redirecting of the impact trajectory,opening new opportunities for engineering and tailoring lightweight materials with target impact *** is achieved through the combined selection of base material and metamaterial design.

关键词： Metamaterial 3D Printing Viscoelasticity Impact Truss Finite element method

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Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

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Computers, Materials & Continua 2021年第5期67卷 1679-1696页

作者： siddhant Banyal Rinky Dwivedi Koyel Datta Gupta Deepak kumar Sharma Fadi Al-Turjman Leonardo Mostarda Department of Instrumentation and Control Netaji Subhas University of TechnologyNew Delhi110078India Department of Computer Science&Engineering Maharaja Surajmal Institute of TechnologyNew Delhi110058India Department of Information Technology Netaji Subhas University of TechnologyNew Delhi110078India Research Center for AI and IoT Near East UniversityNicosiaMersin10Turkey Computer Science Department Camerino UniversityCamerino62032Italy

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death *** nations like the USA became severely hit by SARS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SARS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

关键词： COVID-19 diagnosis deep learning forecasting models machine learning metaheuristics prediction big data pandemic

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