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您的检索式：作者名="Siddhant Kumar"
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| 1 | Inverse-designed spinodoid metamaterials显示文摘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 topologies.Inspired by natural self-assembly processes,spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase separation.Their theoretical parametrization is so intriguingly simple that one can bypass costly phase-field simulations and obtain a rich and seamlessly tunable property space.Counterintuitively,breaking with the periodicity of classical metamaterials is the enabling factor to the large property space and the ability to introduce seamless functional grading.We 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 density.We specifically present biomimetic artificial bone architectures that not only reproduce the properties of trabecular bone accurately but also even geometrically resemble natural bone. | Siddhant Kumar Stephanie Tan Li Zheng Dennis M.Kochmann | 2020 | npj Computational Materials2020，，1： | 1 |
| 2 | Discovering plasticity models without stress data显示文摘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 laws.The 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 experiment.The 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 deformation.For 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 domain.Sparsity promoting regularization is deployed to generate a set of solutions out of which a solution with low cost and high parsimony is automatically selected.Through virtual experiments,we demonstrate the ability of EUCLID to accurately discover several plastic yield surfaces and hardening mechanisms of different complexity. | Moritz Flaschel Siddhant Kumar Laura De Lorenzis | 2022 | npj Computational Materials2022，，1： | 0 |
| 3 | Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19显示文摘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 toll.Soon 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. | Siddhant Banyal Rinky Dwivedi Koyel Datta Gupta Deepak Kumar Sharma Fadi Al-Turjman Leonardo Mostarda | 2021 | Computers, Materials & Continua2021，，5： | 0 |
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