{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:53:27Z","timestamp":1775890407443,"version":"3.50.1"},"reference-count":65,"publisher":"ASME International","issue":"5","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC0206H11357"],"award-info":[{"award-number":["DE-AC0206H11357"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.<\/jats:p>","DOI":"10.1115\/1.4064622","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T16:42:40Z","timestamp":1706632960000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":4,"title":["Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data"],"prefix":"10.1115","volume":"24","author":[{"given":"Anindya","family":"Bhaduri","sequence":"first","affiliation":[{"name":"General Electric Aerospace Research Probabilistic Design Laboratory, , Niskayuna, NY 12309"}]},{"given":"Nesar","family":"Ramachandra","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/05gvnxz63","id-type":"ROR","asserted-by":"publisher"}],"name":"Argonne National Laboratory Computational Science Division, , Lemont, IL 60439"},{"name":"Argonne National Laboratory Computational Science Division, , Lemont, IL 60439"}]},{"given":"Sandipp","family":"Krishnan Ravi","sequence":"additional","affiliation":[{"name":"General Electric Aerospace Research Probabilistic Design Laboratory, , Niskayuna, NY 12309"}]},{"given":"Lele","family":"Luan","sequence":"additional","affiliation":[{"name":"General Electric Aerospace Research Probabilistic Design Laboratory, , Niskayuna, NY 12309"}]},{"given":"Piyush","family":"Pandita","sequence":"additional","affiliation":[{"name":"General Electric Aerospace Research Probabilistic Design Laboratory, , Niskayuna, NY 12309"}]},{"given":"Prasanna","family":"Balaprakash","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01qz5mb56","id-type":"ROR","asserted-by":"publisher"}],"name":"Oak Ridge National Laboratory Computing and Computational Sciences Directorate, , 1 Bethel Valley Rd, Oak Ridge, TN 37831"},{"name":"Oak Ridge National Laboratory Computing and Computational Sciences Directorate, , 1 Bethel Valley Rd, Oak Ridge, TN 37831"}]},{"given":"Mihai","family":"Anitescu","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory Mathematics and Computer Science Division, , Lemont, IL 60439"}]},{"given":"Changjie","family":"Sun","sequence":"additional","affiliation":[{"name":"General Electric Aerospace Research Materials and Mechanical Systems, , Niskayuna, NY 12309"}]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[{"name":"General Electric Aerospace Research Probabilistic Design Laboratory, , Niskayuna, NY 12309"}]}],"member":"33","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"2024041815094712600_CIT0001","article-title":"Ductile Fracture and Ductility","author":"Dodd","year":"1987"},{"key":"2024041815094712600_CIT0002","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/BF02672307","article-title":"Separation of Second Phase Particles in Spheroidized 1045 Steel, Cu-0.6 PCT Cr Alloy, and Maraging Steel in Plastic Straining","volume":"6","author":"Argon","year":"1975","journal-title":"Metall. Trans. A"},{"issue":"8","key":"2024041815094712600_CIT0003","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1016\/0001-6160(81)90185-1","article-title":"A Model of Ductile Fracture Based on the Nucleation and Growth of Voids","volume":"29","author":"Le Roy","year":"1981","journal-title":"Acta. Metall."},{"issue":"4","key":"2024041815094712600_CIT0004","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1115\/1.3422899","article-title":"Void Growth in an Elastic-Plastic Medium","volume":"39","author":"Needleman","year":"1972","journal-title":"ASME J. Appl. Mech."},{"key":"2024041815094712600_CIT0005","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/BF00036191","article-title":"Influence of Voids on Shear Band Instabilities Under Plane Strain Conditions","volume":"17","author":"Tvergaard","year":"1981","journal-title":"Int. J. Fracture"},{"key":"2024041815094712600_CIT0006","volume-title":"Ductile Fracture of Metals","author":"Thomason","year":"1990"},{"issue":"13","key":"2024041815094712600_CIT0007","doi-asserted-by":"publisher","first-page":"3997","DOI":"10.1016\/j.actamat.2004.05.015","article-title":"A Model for Ductile Fracture Based on Internal Necking of Spheroidal Voids","volume":"52","author":"Ragab","year":"2004","journal-title":"Acta Mater."},{"issue":"1\u20133","key":"2024041815094712600_CIT0008","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.jmatprotec.2007.10.054","article-title":"Metal Ductility at Low Stress Triaxiality Application to Sheet Trimming","volume":"203","author":"Bacha","year":"2008","journal-title":"J. Mater. Process. Technol."},{"key":"2024041815094712600_CIT0009","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/BF00016582","article-title":"The Effect of Void Size and Distribution on Ductile Fracture","volume":"16","author":"Melander","year":"1980","journal-title":"Int. J. Fracture"},{"issue":"18\u201319","key":"2024041815094712600_CIT0010","doi-asserted-by":"publisher","first-page":"5097","DOI":"10.1016\/j.ijsolstr.2005.02.028","article-title":"On Ductile Fracture Initiation Toughness: Effects of Void Volume Fraction, Void Shape and Void Distribution","volume":"42","author":"Gao","year":"2005","journal-title":"Int. J. Solids Struct."},{"key":"2024041815094712600_CIT0011","article-title":"Response Surface Methodology: Process and Product Optimization Using Designed Experiments","author":"Gunst","year":"1996"},{"key":"2024041815094712600_CIT0012","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.probengmech.2017.11.002","article-title":"An Efficient Adaptive Sparse Grid Collocation Method Through Derivative Estimation","volume":"51","author":"Bhaduri","year":"2018","journal-title":"Probab. Eng. Mech."},{"issue":"3","key":"2024041815094712600_CIT0013","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1080\/08927022.2019.1688325","article-title":"Free Energy Calculation Using Space Filled Design and Weighted Reconstruction: A Modified Single Sweep Approach","volume":"46","author":"Bhaduri","year":"2020","journal-title":"Mol. Simul."},{"key":"2024041815094712600_CIT0014","doi-asserted-by":"publisher","first-page":"103024","DOI":"10.1016\/j.probengmech.2020.103024","article-title":"On the Usefulness of Gradient Information in Surrogate Modeling: Application to Uncertainty Propagation in Composite Material Models","volume":"60","author":"Bhaduri","year":"2020","journal-title":"Probab. Eng. Mech."},{"key":"2024041815094712600_CIT0015","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods","author":"Cristianini","year":"2000"},{"key":"2024041815094712600_CIT0016","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/978-94-011-5014-9_23","article-title":"Prediction With Gaussian Processes: From Linear Regression to Linear Prediction and Beyond","volume-title":"Learning in Graphical Models","author":"Williams","year":"1998"},{"issue":"11","key":"2024041815094712600_CIT0017","doi-asserted-by":"publisher","first-page":"04021087","DOI":"10.1061\/(ASCE)EM.1943-7889.0001996","article-title":"Probabilistic Modeling of Discrete Structural Response With Application to Composite Plate Penetration Models","volume":"147","author":"Bhaduri","year":"2021","journal-title":"J. Eng. Mech."},{"key":"2024041815094712600_CIT0018","doi-asserted-by":"publisher","first-page":"114007","DOI":"10.1016\/j.cma.2021.114007","article-title":"Surrogate-Based Sequential Bayesian Experimental Design Using Non-Stationary Gaussian Processes","volume":"385","author":"Pandita","year":"2021","journal-title":"Comput. Meth. Appl. Mech. Eng."},{"issue":"7","key":"2024041815094712600_CIT0019","doi-asserted-by":"publisher","first-page":"074502","DOI":"10.1115\/1.4050246","article-title":"Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications","volume":"143","author":"Pandita","year":"2021","journal-title":"ASME J. Mech. Des."},{"key":"2024041815094712600_CIT0020","first-page":"V002T02A040","article-title":"A Comparative Study of Surrogate Modeling of Nonlinear Dynamic Systems","volume-title":"International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 86212","author":"Zhao","year":"2022"},{"key":"2024041815094712600_CIT0021","first-page":"V002T02A038","article-title":"Integrated Computational Materials Engineering With Monotonic Gaussian Processes","volume-title":"International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 86212","author":"Tran","year":"2022"},{"key":"2024041815094712600_CIT0022","doi-asserted-by":"publisher","first-page":"103201","DOI":"10.1016\/j.marstruc.2022.103201","article-title":"Data-Driven Modeling of Multiaxial Fatigue in Frequency Domain","volume":"84","author":"Ravi","year":"2022","journal-title":"Marine Struct."},{"key":"2024041815094712600_CIT0023","article-title":"Data-Driven Modeling of Multiaxial Fatigue of Structures in Frequency Domain","author":"Ravi","year":"2022"},{"key":"2024041815094712600_CIT0024","doi-asserted-by":"publisher","first-page":"108516","DOI":"10.1016\/j.ymssp.2021.108516","article-title":"A Multi-Axial Vibration Fatigue Evaluation Procedure for Welded Structures in Frequency Domain","volume":"167","author":"Pei","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"2024041815094712600_CIT0025","doi-asserted-by":"publisher","first-page":"100183","DOI":"10.1016\/j.mlblux.2023.100183","article-title":"Data-Driven Predictive Modeling of Fecral Oxidation","author":"Roy","year":"2023","journal-title":"Mater. Lett.: X"},{"key":"2024041815094712600_CIT0026","doi-asserted-by":"publisher","DOI":"10.2514\/6.2023-0528","article-title":"On Uncertainty Quantification in Materials Modeling and Discovery: Applications of GE\u2019s BHM and IDACE","author":"Ravi","year":"2023"},{"issue":"8","key":"2024041815094712600_CIT0027","doi-asserted-by":"publisher","first-page":"112440","DOI":"10.1016\/j.commatsci.2023.112440","article-title":"Elucidating Precipitation in Fecral Alloys Through Explainable Ai: A Case Study","volume":"230","author":"Roy","year":"2022","journal-title":"Comput. Mater. Sci."},{"key":"2024041815094712600_CIT0028","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1557\/s43579-022-00315-0","article-title":"Understanding Oxidation of Fe-Cr-Al Alloys Through Explainable Artificial Intelligence","volume":"13","author":"Roy","year":"2023","journal-title":"MRS Commun."},{"key":"2024041815094712600_CIT0029","first-page":"4","article-title":"Gaussian Processes for Machine Learning","author":"Williams","year":"2006"},{"key":"2024041815094712600_CIT0030","article-title":"When Gaussian Process Meets Big Data: A Review of Scalable GPS","volume-title":"Preprint","author":"Liu","year":"2018"},{"key":"2024041815094712600_CIT0031","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.5555\/1046920.1194909","article-title":"A Unifying View of Sparse Approximate Gaussian Process Regression","volume":"6","author":"Qui\u00f1onero-Candela","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"2024041815094712600_CIT0032","volume-title":"Stochastic Finite Elements: A Spectral Approach.","author":"Ghanem","year":"2003"},{"key":"2024041815094712600_CIT0033","doi-asserted-by":"publisher","first-page":"A1500","DOI":"10.1137\/130916138","article-title":"Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces","volume":"36","author":"Constantine","year":"2014","journal-title":"SIAM J. Scientific Comput."},{"key":"2024041815094712600_CIT0034","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611973860","volume-title":"Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies","author":"Constantine","year":"2015"},{"key":"2024041815094712600_CIT0035","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ijfatigue.2012.07.012","article-title":"Surrogate Modeling of 3D Crack Growth","volume":"47","author":"Hombal","year":"2013","journal-title":"Int. J. Fatigue"},{"issue":"1","key":"2024041815094712600_CIT0036","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.cam.2012.06.010","article-title":"Gradient-Enhanced Surrogate Modeling Based on Proper Orthogonal Decomposition","volume":"237","author":"Zimmermann","year":"2013","journal-title":"J. Comput. Appl. Math."},{"key":"2024041815094712600_CIT0037","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.jcp.2016.05.039","article-title":"Gaussian Processes With Built-In Dimensionality Reduction: Applications to High-Dimensional Uncertainty Propagation","volume":"321","author":"Tripathy","year":"2016","journal-title":"J. Comput. Phys."},{"key":"2024041815094712600_CIT0038","first-page":"V08BT25A009","article-title":"Efficient Surrogate Modeling for Turbine Blade Row Cyclic Symmetric Mode Shapes","author":"Bhaduri","year":"2022"},{"issue":"1","key":"2024041815094712600_CIT0039","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1002\/nme.6856","article-title":"Probabilistic Learning on Manifolds (PLOM) With Partition","volume":"123","author":"Soize","year":"2022","journal-title":"Int. J. Numer. Methods Eng."},{"key":"2024041815094712600_CIT0040","article-title":"Nonlinear Dimension Reduction for Surrogate Modeling Using Gradient Information","volume-title":"Preprint","author":"Bigoni","year":"2021"},{"issue":"11","key":"2024041815094712600_CIT0041","doi-asserted-by":"publisher","first-page":"111408","DOI":"10.1115\/1.4037309","article-title":"A Convolutional Neural Network Model for Predicting a Product\u2019s Function, Given Its Form","volume":"139","author":"Dering","year":"2017","journal-title":"ASME J. Mech. Des."},{"issue":"5","key":"2024041815094712600_CIT0042","doi-asserted-by":"publisher","first-page":"051004","DOI":"10.1115\/1.4056806","article-title":"Manufacturing Process Classification Based on Distance Rotationally Invariant Convolutions","volume":"23","author":"Wang","year":"2023","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"1","key":"2024041815094712600_CIT0043","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1115\/1.4044097","article-title":"Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks","volume":"20","author":"Nie","year":"2020","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"2","key":"2024041815094712600_CIT0044","doi-asserted-by":"publisher","first-page":"021008","DOI":"10.1115\/1.4054559","article-title":"Sub-Second Prediction of the Heatmap of Powder-Beds in Additive Manufacturing Using Deep Encoder-Decoder Convolutional Neural Networks","volume":"23","author":"Sofi","year":"2023","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"3","key":"2024041815094712600_CIT0045","doi-asserted-by":"publisher","first-page":"034501","DOI":"10.1115\/1.4054687","article-title":"Supermeshing: Boosting the Mesh Density of Stress Field in Plane-Strain Problems Using Deep Learning Method","volume":"23","author":"Xu","year":"2023","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"3","key":"2024041815094712600_CIT0046","doi-asserted-by":"publisher","first-page":"031005","DOI":"10.1115\/1.4053078","article-title":"Segmentation of Additive Manufacturing Defects Using U-Net","volume":"22","author":"Wong","year":"2022","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"3","key":"2024041815094712600_CIT0047","doi-asserted-by":"publisher","first-page":"031008","DOI":"10.1115\/1.4053077","article-title":"Prediction of Mechanical Properties of Three-Dimensional Printed Lattice Structures Through Machine Learning","volume":"22","author":"Ma","year":"2022","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"2","key":"2024041815094712600_CIT0048","doi-asserted-by":"publisher","first-page":"021004","DOI":"10.1115\/1.4045293","article-title":"Dilated Convolution Neural Network for Remaining Useful Life Prediction","volume":"20","author":"Xu","year":"2020","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"1","key":"2024041815094712600_CIT0049","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1115\/1.4044097","article-title":"Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks","volume":"20","author":"Nie","year":"2019","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"key":"2024041815094712600_CIT0050","first-page":"115100R","volume-title":"Applications of Digital Image Processing XLIII, Vol. 11510","author":"de Le\u00f3n","year":"2020"},{"issue":"7","key":"2024041815094712600_CIT0051","doi-asserted-by":"publisher","first-page":"D50","DOI":"10.1364\/ao.444563","article-title":"PhotoelastNet: A Deep Convolutional Neural Network for Evaluating the Stress Field by Using a Single Color Photoelasticity Image","volume":"61","author":"Bri\u00f1ez-de Le\u00f3n","year":"2022","journal-title":"Appl. Opt."},{"key":"2024041815094712600_CIT0052","first-page":"42","article-title":"Deephyper: Asynchronous Hyperparameter Search for Deep Neural Networks","author":"Balaprakash","year":"2018"},{"key":"2024041815094712600_CIT0053","volume-title":"Concepts and Applications of Finite Element Analysis","author":"Cook","year":"2007"},{"key":"2024041815094712600_CIT0054","first-page":"25","article-title":"Analysis of a Plate With a Circular Hole by Fem","author":"Mekalke","year":"2012","journal-title":"J. Mech. Civil Eng."},{"issue":"1","key":"2024041815094712600_CIT0055","first-page":"57","article-title":"Application of Finite Element Analysis of Thin Steel Plate With Holes","volume":"18","author":"Nikoli\u0107","year":"2011","journal-title":"Tehni\u010dki vjesnik"},{"issue":"1\u20132","key":"2024041815094712600_CIT0056","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.msea.2008.04.078","article-title":"Finite Element Analysis for Stress Concentration and Deflection in Isotropic, Orthotropic and Laminated Composite Plates With Central Circular Hole Under Transverse Static Loading","volume":"498","author":"Jain","year":"2008","journal-title":"Mater. Sci. Eng. A"},{"issue":"3","key":"2024041815094712600_CIT0057","first-page":"387","article-title":"Stress and Displacement Analysis of a Rectangular Plate With Central Elliptical Hole","volume":"3","author":"Gunwant","year":"2013","journal-title":"Int. J. Eng. Innov. Technol."},{"key":"2024041815094712600_CIT0058","article-title":"Stress Analysis of Steel Plate Having Holes of Various Shapes, Sizes and Orientations Using Finite Element Method","author":"Hasan","year":"2009"},{"key":"2024041815094712600_CIT0059","first-page":"012067","article-title":"Analysis of Stress Concentration at the Edge of Hole in Plates With Different Widths by Using FEM","volume-title":"IOP Conf. Series Mater. Sci. Eng.","author":"Safaei","year":"2022"},{"issue":"1","key":"2024041815094712600_CIT0060","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning Deep Architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Mach. Learn."},{"key":"2024041815094712600_CIT0061","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"2024041815094712600_CIT0062","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger","year":"2015"},{"key":"2024041815094712600_CIT0063","doi-asserted-by":"publisher","first-page":"109879","DOI":"10.1016\/j.compositesb.2022.109879","article-title":"Stress Field Prediction in Fiber-Reinforced Composite Materials Using a Deep Learning Approach","volume":"238","author":"Bhaduri","year":"2022","journal-title":"Composites Part B: Eng."},{"key":"2024041815094712600_CIT0064","article-title":"Utilizing UNet for the Future Traffic Map Prediction Task Traffic4cast Challenge 2020","volume-title":"Preprint","author":"Choi","year":"2020"},{"key":"2024041815094712600_CIT0065","article-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems","volume-title":"Software Available From","author":"Abadi","year":"2015"}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/24\/5\/051008\/7329413\/jcise_24_5_051008.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/24\/5\/051008\/7329413\/jcise_24_5_051008.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T15:10:00Z","timestamp":1713453000000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/24\/5\/051008\/1195153\/Efficient-Mapping-Between-Void-Shapes-and-Stress"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,5]]},"references-count":65,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4064622","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,5]]},"article-number":"051008"}}