{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T20:46:29Z","timestamp":1775249189147,"version":"3.50.1"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["UMO-2021\/42\/E\/ST5\/00339"],"award-info":[{"award-number":["UMO-2021\/42\/E\/ST5\/00339"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00366-025-02117-z","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T11:44:34Z","timestamp":1740743074000},"page":"2593-2617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Foretelling microstructural interface with multi-generational convolutional-LSTM framework"],"prefix":"10.1007","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5527-1348","authenticated-orcid":false,"given":"Upadesh","family":"Subedi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nele","family":"Moelans","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomasz","family":"T\u00e1nski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anil","family":"Kunwar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"issue":"2","key":"2117_CR1","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.calphad.2007.11.003","volume":"32","author":"N Moelans","year":"2008","unstructured":"Moelans N, Blanpain B, Wollants P (2008) An introduction to phase-field modeling of microstructure evolution. CALPHAD: Comput Coupl Phase Diagr Thermochem 32(2):268\u2013294. https:\/\/doi.org\/10.1016\/j.calphad.2007.11.003","journal-title":"CALPHAD: Comput Coupl Phase Diagr Thermochem"},{"issue":"8","key":"2117_CR2","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1016\/j.engfailanal.2007.11.003","volume":"15","author":"Y Prawoto","year":"2008","unstructured":"Prawoto Y, Ikeda M, Manville SK, Nishikawa A (2008) Design and failure modes of automotive suspension springs. Eng Fail Anal 15(8):1155\u20131174. https:\/\/doi.org\/10.1016\/j.engfailanal.2007.11.003","journal-title":"Eng Fail Anal"},{"key":"2117_CR3","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.ijfatigue.2019.03.019","volume":"124","author":"M Dallago","year":"2019","unstructured":"Dallago M, Winiarski B, Zanini F, Carmignato S, Benedetti M (2019) On the effect of geometrical imperfections and defects on the fatigue strength of cellular lattice structures additively manufactured via Selective Laser Melting. Int J Fatigue 124:348\u2013360. https:\/\/doi.org\/10.1016\/j.ijfatigue.2019.03.019","journal-title":"Int J Fatigue"},{"key":"2117_CR4","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1016\/j.jmbbm.2016.09.035","volume":"65","author":"F Li","year":"2017","unstructured":"Li F, Li J, Huang T, Kou H, Zhou L (2017) Compression fatigue behavior and failure mechanism of porous titanium for biomedical applications. J Mech Behav Biomed Mater 65:814\u2013823. https:\/\/doi.org\/10.1016\/j.jmbbm.2016.09.035","journal-title":"J Mech Behav Biomed Mater"},{"issue":"3","key":"2117_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s11668-996-0007-9","volume":"4","author":"B Aksakal","year":"2004","unstructured":"Aksakal B, Yildirim OS, Gul H (2004) Metallurgical failure analysis of various implant materials used in orthopedic applications. J Fail Anal Prev 4(3):17\u201323. https:\/\/doi.org\/10.1007\/s11668-996-0007-9","journal-title":"J Fail Anal Prev"},{"key":"2117_CR6","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1016\/j.jmrt.2022.01.050","volume":"17","author":"K Moghadasi","year":"2022","unstructured":"Moghadasi K, Mohd Isa MS, Ariffin MA, Mohdjamil MZ, Raja S, Wu B, Yamani M, Bin Muhamad MR, Yusof F, Jamaludin MF, AbKarim MS, Abdul Razak B, Yusoff N (2022) A review on biomedical implant materials and the effect of friction stir based techniques on their mechanical and tribological properties. J Market Res 17:1054\u20131121. https:\/\/doi.org\/10.1016\/j.jmrt.2022.01.050","journal-title":"J Market Res"},{"key":"2117_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.msea.2022.143179","volume":"844","author":"A Mohammadi","year":"2022","unstructured":"Mohammadi A, Edalati P, Arita M, Bae JW, Kim HS, Edalati K (2022) Microstructure and defect effects on strength and hydrogen embrittlement of high-entropy alloy CrMnFeCoNi processed by high-pressure torsion. Mater Sci Eng A 844:143179. https:\/\/doi.org\/10.1016\/j.msea.2022.143179","journal-title":"Mater Sci Eng A"},{"issue":"12","key":"2117_CR8","doi-asserted-by":"publisher","first-page":"3057","DOI":"10.1016\/s1359-6454(02)00084-8","volume":"50","author":"CE Krill","year":"2002","unstructured":"Krill CE, Chen LQ (2002) Computer simulation of 3-D grain growth using a phase-field model. Acta Mater 50(12):3057\u20133073. https:\/\/doi.org\/10.1016\/s1359-6454(02)00084-8","journal-title":"Acta Mater"},{"issue":"1","key":"2117_CR9","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1080\/14786431003773015","volume":"91","author":"R Spatschek","year":"2011","unstructured":"Spatschek R, Brener E, Karma A (2011) Phase field modeling of crack propagation. Phil Mag 91(1):75\u201395. https:\/\/doi.org\/10.1080\/14786431003773015","journal-title":"Phil Mag"},{"key":"2117_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmps.2019.103684","volume":"132","author":"YS Lo","year":"2019","unstructured":"Lo YS, Borden MJ, Ravi-Chandar K, Landis CM (2019) A phase-field model for fatigue crack growth. J Mech Phys Solids 132:103684. https:\/\/doi.org\/10.1016\/j.jmps.2019.103684","journal-title":"J Mech Phys Solids"},{"issue":"3","key":"2117_CR11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.79.031926","volume":"79","author":"JS Lowengrub","year":"2009","unstructured":"Lowengrub JS, Ratz A, Voigt A (2009) Phase-field modeling of the dynamics of multicomponent vesicles: spinodal decomposition, coarsening, budding, and fission. Phys Rev E Stat Nonlinear Soft Matter Phys 79(3):031926. https:\/\/doi.org\/10.1103\/PhysRevE.79.031926","journal-title":"Phys Rev E Stat Nonlinear Soft Matter Phys"},{"issue":"1","key":"2117_CR12","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s41524-021-00524-6","volume":"7","author":"M Yang","year":"2021","unstructured":"Yang M, Wang L, Yan W (2021) Phase-field modeling of grain evolutions in additive manufacturing from nucleation, growth, to coarsening. NPJ Comput Mater 7(1):56. https:\/\/doi.org\/10.1038\/s41524-021-00524-6","journal-title":"NPJ Comput Mater"},{"issue":"6","key":"2117_CR13","doi-asserted-by":"publisher","first-page":"964","DOI":"10.3390\/met12060964","volume":"12","author":"U Subedi","year":"2022","unstructured":"Subedi U, Coutinho YA, Malla PB, Gyanwali K, Kunwar A (2022) Automatic featurization aided data-driven method for estimating the presence of intermetallic phase in multi-principal element alloys. Metals 12(6):964. https:\/\/doi.org\/10.3390\/met12060964","journal-title":"Metals"},{"issue":"11","key":"2117_CR14","doi-asserted-by":"publisher","first-page":"1884","DOI":"10.3390\/met12111884","volume":"12","author":"U Subedi","year":"2022","unstructured":"Subedi U, Poudel S, Gyanwali K, Amorim Coutinho Y, Matula G, Kunwar A (2022) State-of-the-art review on the aspects of martensitic alloys studied via machine learning. Metals 12(11):1884. https:\/\/doi.org\/10.3390\/met12111884","journal-title":"Metals"},{"key":"2117_CR15","doi-asserted-by":"publisher","unstructured":"Poudel S, et al (2024) AlloyManufacturingNet for discovery and design of hardness-elongation synergy in multi-principal element alloys. Eng Appl Artif Intell 132:107902. https:\/\/doi.org\/10.1016\/j.engappai.2024.107902","DOI":"10.1016\/j.engappai.2024.107902"},{"key":"2117_CR16","doi-asserted-by":"publisher","unstructured":"Mamo HB, et al (2024)\u00a0Prototyping Ti2Cu intermetallic grain growth heterogeneously in Ti6Al4V matrix through laser additive manufacturing. Mater Des 246:113312. https:\/\/doi.org\/10.1016\/j.matdes.2024.113312","DOI":"10.1016\/j.matdes.2024.113312"},{"key":"2117_CR17","doi-asserted-by":"publisher","unstructured":"Poudel S, et al (2024) Unraveling elastochemical effects in microstructural evolution of Al\u2013Cu\u2013Ni system through DFT-informed multi-phase field simulations. Int J Solids Struct 300:112894. https:\/\/doi.org\/10.1016\/j.ijsolstr.2024.112894","DOI":"10.1016\/j.ijsolstr.2024.112894"},{"key":"2117_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnucmat.2022.154147","volume":"574","author":"C Vivek Bhave","year":"2023","unstructured":"Vivek Bhave C, Zheng G, Sridharan K, Schwen D, Tonks MR (2023) An electrochemical mesoscale tool for modeling the corrosion of structural alloys by molten salt. J Nucl Mater 574:154147. https:\/\/doi.org\/10.1016\/j.jnucmat.2022.154147","journal-title":"J Nucl Mater"},{"issue":"3","key":"2117_CR19","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1016\/j.engfailanal.2010.12.025","volume":"18","author":"H Yang","year":"2011","unstructured":"Yang H, Bao R, Zhang J, Peng L, Fei B (2011) Creep-fatigue crack growth behaviour of a nickel-based powder metallurgy superalloy under high temperature. Eng Fail Anal 18(3):1058\u20131066. https:\/\/doi.org\/10.1016\/j.engfailanal.2010.12.025","journal-title":"Eng Fail Anal"},{"issue":"1","key":"2117_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1146\/annurev.matsci.37.052506.084250","volume":"37","author":"J Chevalier","year":"2007","unstructured":"Chevalier J, Gremillard L, Deville S (2007) Low-temperature degradation of zirconia and implications for biomedical implants. Annu Rev Mater Res 37(1):1\u201332. https:\/\/doi.org\/10.1146\/annurev.matsci.37.052506.084250","journal-title":"Annu Rev Mater Res"},{"key":"2117_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmbbm.2020.103815","volume":"108","author":"VSA Challa","year":"2020","unstructured":"Challa VSA, Nune KC, Gong N, Misra RDK (2020) The significant impact of mechanically-induced phase transformation on cellular functionality of biomedical austenitic stainless steel. J Mech Behav Biomed Mater 108:103815. https:\/\/doi.org\/10.1016\/j.jmbbm.2020.103815","journal-title":"J Mech Behav Biomed Mater"},{"key":"2117_CR22","doi-asserted-by":"crossref","unstructured":"Poudel, S., et al., PiezoTensorNet: Crystallography informed multi-scale hierarchical machine learning model for rapid piezoelectric performance finetuning Applied Energy 361, 122901 (2024) https:\/\/doi.org\/10.1016\/j.apenergy.2024.122901","DOI":"10.1016\/j.apenergy.2024.122901"},{"issue":"1","key":"2117_CR23","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/s41524-020-00471-8","volume":"7","author":"D Oca Zapiain","year":"2021","unstructured":"Oca Zapiain D, Stewart JA, Dingreville R (2021) Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods. NPJ Comput Mater 7(1):3. https:\/\/doi.org\/10.1038\/s41524-020-00471-8","journal-title":"NPJ Comput Mater"},{"issue":"5","key":"2117_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100243","volume":"2","author":"K Yang","year":"2021","unstructured":"Yang K, Cao Y, Zhang Y, Fan S, Tang M, Aberg D, Sadigh B, Zhou F (2021) Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks. Patterns 2(5):100243. https:\/\/doi.org\/10.1016\/j.patter.2021.100243","journal-title":"Patterns"},{"key":"2117_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115128","volume":"397","author":"C Hu","year":"2022","unstructured":"Hu C, Martin S, Dingreville R (2022) Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space. Comput Methods Appl Mech Eng 397:115128. https:\/\/doi.org\/10.1016\/j.cma.2022.115128","journal-title":"Comput Methods Appl Mech Eng"},{"key":"2117_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2023.112110","volume":"223","author":"AA Kazemzadeh Farizhandi","year":"2023","unstructured":"Kazemzadeh Farizhandi AA, Mamivand M (2023) Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network. Comput Mater Sci 223:112110. https:\/\/doi.org\/10.1016\/j.commatsci.2023.112110","journal-title":"Comput Mater Sci"},{"key":"2117_CR27","doi-asserted-by":"crossref","unstructured":"Ahmad O, Kumar N, Mukherjee R, Bhowmick S (2023) Accelerating microstructure modelling via machine learning: a new method combining Autoencoder and ConvLSTM. arXiv:2305.00938","DOI":"10.1103\/PhysRevMaterials.7.083802"},{"issue":"10","key":"2117_CR28","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1016\/j.nucengdes.2009.05.021","volume":"239","author":"D Gaston","year":"2009","unstructured":"Gaston D, Newman C, Hansen G, Lebrun-Grandi\u00e9 D (2009) MOOSE: a parallel computational framework for coupled systems of nonlinear equations. Nucl Eng Des 239(10):1768\u20131778. https:\/\/doi.org\/10.1016\/j.nucengdes.2009.05.021","journal-title":"Nucl Eng Des"},{"issue":"1","key":"2117_CR29","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.commatsci.2011.07.028","volume":"51","author":"MR Tonks","year":"2012","unstructured":"Tonks MR, Gaston D, Millett PC, Andrs D, Talbot P (2012) An object-oriented finite element framework for multiphysics phase field simulations. Comput Mater Sci 51(1):20\u201329. https:\/\/doi.org\/10.1016\/j.commatsci.2011.07.028","journal-title":"Comput Mater Sci"},{"key":"2117_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2022.101202","volume":"20","author":"AD Lindsay","year":"2022","unstructured":"...Lindsay AD, Gaston DR, Permann CJ, Miller JM, Andr\u0161 D, Slaughter AE, Kong F, Hansel J, Carlsen RW, Icenhour C, Harbour L, Giudicelli GL, Stogner RH, German P, Badger J, Biswas S, Chapuis L, Green C, Hales J, Hu T, Jiang W, Jung YS, Matthews C, Miao Y, Novak A, Peterson JW, Prince ZM, Rovinelli A, Schunert S, Schwen D, Spencer BW, Veeraraghavan S, Recuero A, Yushu D, Wang Y, Wilkins A, Wong C (2022) 2.0\u2014moose: Enabling massively parallel multiphysics simulation. SoftwareX 20:101202. https:\/\/doi.org\/10.1016\/j.softx.2022.101202","journal-title":"SoftwareX"},{"key":"2117_CR31","doi-asserted-by":"publisher","unstructured":"Aagesen LK, Gao Y, Schwen D, Ahmed K (2018) Grand-potential-based phase-field model for multiple phases, grains, and chemical components. Phys Rev E 98(2). https:\/\/doi.org\/10.1103\/PhysRevE.98.023309","DOI":"10.1103\/PhysRevE.98.023309"},{"key":"2117_CR32","doi-asserted-by":"publisher","DOI":"10.1557\/s43577-022-00443-x","author":"AS Iquebal","year":"2023","unstructured":"Iquebal AS, Wu P, Sarfraz A, Ankit K (2023) Emulating the evolution of phase separating microstructures using low-dimensional tensor decomposition and nonlinear regression. MRS Bull. https:\/\/doi.org\/10.1557\/s43577-022-00443-x","journal-title":"MRS Bull"},{"key":"2117_CR33","doi-asserted-by":"publisher","unstructured":"Iwamatsu M (2008) Direct numerical simulation of homogeneous nucleation and growth in a phase-field model using cell dynamics method. J Chem Phys. https:\/\/doi.org\/10.1063\/1.2883652","DOI":"10.1063\/1.2883652"},{"key":"2117_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2023.112187","volume":"224","author":"P Wu","year":"2023","unstructured":"Wu P, Iquebal AS, Ankit K (2023) Emulating microstructural evolution during spinodal decomposition using a tensor decomposed convolutional and recurrent neural network. Comput Mater Sci 224:112187. https:\/\/doi.org\/10.1016\/j.commatsci.2023.112187","journal-title":"Comput Mater Sci"},{"key":"2117_CR35","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jcp.2012.12.001","volume":"236","author":"L Zhang","year":"2013","unstructured":"Zhang L, Tonks MR, Gaston D, Peterson JW, Andrs D, Millett PC, Biner BS (2013) A quantitative comparison between and elements for solving the Cahn\u2013Hilliard equation. J Comput Phys 236:74\u201380. https:\/\/doi.org\/10.1016\/j.jcp.2012.12.001","journal-title":"J Comput Phys"},{"key":"2117_CR36","doi-asserted-by":"publisher","unstructured":"Nolte DD (2019) Introduction to modern dynamics. Oxford University Press. https:\/\/doi.org\/10.1093\/oso\/9780198844624.001.0001","DOI":"10.1093\/oso\/9780198844624.001.0001"},{"key":"2117_CR37","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/j.cma.2017.02.011","volume":"318","author":"X Yang","year":"2017","unstructured":"Yang X, Ju L (2017) Linear and unconditionally energy stable schemes for the binary fluid-surfactant phase field model. Comput Methods Appl Mech Eng 318:1005\u20131029. https:\/\/doi.org\/10.1016\/j.cma.2017.02.011","journal-title":"Comput Methods Appl Mech Eng"},{"key":"2117_CR38","doi-asserted-by":"publisher","unstructured":"Yang X, Xiao J (2019) On linear and unconditionally energy stable algorithms for variable mobility Cahn\u2013Hilliard type equation with logarithmic Flory\u2013Huggins potential. Commun Comput Phys. https:\/\/doi.org\/10.4208\/cicp.OA-2017-0259","DOI":"10.4208\/cicp.OA-2017-0259"},{"issue":"11","key":"2117_CR39","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1080\/09500830701496560","volume":"87","author":"K Binder","year":"2007","unstructured":"Binder K (2007) Double-well thermodynamic potentials and spinodal curves: how real are they? Philos Mag Lett 87(11):799\u2013811. https:\/\/doi.org\/10.1080\/09500830701496560","journal-title":"Philos Mag Lett"},{"issue":"2","key":"2117_CR40","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s10915-020-01127-x","volume":"82","author":"X Wang","year":"2020","unstructured":"Wang X, Kou J, Cai J (2020) Stabilized energy factorization approach for Allen\u2013Cahn equation with logarithmic Flory\u2013Huggins potential. J Sci Comput 82(2):25. https:\/\/doi.org\/10.1007\/s10915-020-01127-x","journal-title":"J Sci Comput"},{"key":"2117_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.surfcoat.2020.125970","volume":"398","author":"MM Hussain","year":"2020","unstructured":"Hussain MM, Ma H, Huang M, Gao Z, Cao J, Wang C, Dong C, Wang Y, Kunwar A (2020) Fabrication of cerium myristate coating for a mechanochemically robust modifier-free superwettability system to enhance the corrosion resistance on 316L steel by one-step electrodeposition. Surf Coat Technol 398:125970. https:\/\/doi.org\/10.1016\/j.surfcoat.2020.125970","journal-title":"Surf Coat Technol"},{"key":"2117_CR42","doi-asserted-by":"publisher","unstructured":"Kunwar A, Tonks MR, Shang S, Song X, Wang Y, Ma H (2017) Quantitative polynomial free energy based phase field model for void motion and evolution in Sn under thermal gradient. In: 2017 18th International Conference on Electronic Packaging Technology (ICEPT), pp. 1502\u20131507. IEEE. https:\/\/doi.org\/10.1109\/ICEPT.2017.8046720 . http:\/\/ieeexplore.ieee.org\/document\/8046720\/","DOI":"10.1109\/ICEPT.2017.8046720"},{"key":"2117_CR43","doi-asserted-by":"publisher","unstructured":"Bandyopadhyay AK, Ray PC, Gopalan V (2006) Dynamical systems analysis for polarization in ferroelectrics. J Appl Phys. https:\/\/doi.org\/10.1063\/1.2388124","DOI":"10.1063\/1.2388124"},{"issue":"9","key":"2117_CR44","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.3390\/met10091209","volume":"10","author":"W Shin","year":"2020","unstructured":"Shin W, Lee J, Chang K (2020) The effects of inhomogeneous elasticity and dislocation on thermodynamics and the kinetics of the spinodal decomposition of a Fe\u2013Cr system: a phase-field study. Metals 10(9):1209. https:\/\/doi.org\/10.3390\/met10091209","journal-title":"Metals"},{"key":"2117_CR45","doi-asserted-by":"crossref","unstructured":"Ahrens J, Geveci B, Law C (2005) ParaView: An End-User Tool for Large Data Visualization ParaViewWeb View project. In: Los Alamos National Laboratory, vol 836. Elesvier. https:\/\/datascience.dsscale.org\/wp-content\/uploads\/2016\/06\/ParaView.pdf","DOI":"10.1016\/B978-012387582-2\/50038-1"},{"issue":"1","key":"2117_CR46","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1063\/1.89819","volume":"32","author":"SP Singhal","year":"1978","unstructured":"Singhal SP, Herman H, Hirvonen JK (1978) Spinodal decomposition in amorphous Au-implanted Pt. Appl Phys Lett 32(1):25\u201326. https:\/\/doi.org\/10.1063\/1.89819","journal-title":"Appl Phys Lett"},{"key":"2117_CR47","doi-asserted-by":"publisher","unstructured":"Otis R, Liu Z-K (2017) pycalphad: CALPHAD-based computational thermodynamics in Python. J Open Res Softw 5(1):1. https:\/\/doi.org\/10.5334\/jors.140","DOI":"10.5334\/jors.140"},{"key":"2117_CR48","unstructured":"Lecun Y, Bengio Y (1995) In: Arbib MA (ed) Convolutional networks for images, speech and time series. The MIT Press, pp 255\u2013258"},{"key":"2117_CR49","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"2117_CR50","unstructured":"Olah C (2015) Understanding LSTM Networks. http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/"},{"key":"2117_CR51","unstructured":"Elsworth S, Guttel S (2020) Time series forecasting using lstm networks: a symbolic approach. arXiv:2003.05672"},{"key":"2117_CR52","unstructured":"Bird JJ, Faria DR, Ekart A, Premebida C, Ayrosa PPS (2020) Lstm and gpt-2 synthetic speech transfer learning for speaker recognition to overcome data scarcity. arXiv:2007.00659"},{"issue":"13","key":"2117_CR53","doi-asserted-by":"publisher","first-page":"14290","DOI":"10.1109\/JSEN.2020.3023471","volume":"21","author":"SP Singh","year":"2021","unstructured":"Singh SP, Wang L, Gupta S, Gulyas B, Padmanabhan P (2021) Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors. IEEE Sens J 21(13):14290\u201314299. https:\/\/doi.org\/10.1109\/JSEN.2020.3023471","journal-title":"IEEE Sens J"},{"key":"2117_CR54","doi-asserted-by":"publisher","unstructured":"Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M (2014) Medical image classification with convolutional neural network. In: 2014 13th international conference on control automation robotics and vision, ICARCV 2014, pp 844\u2013848. IEEE. https:\/\/doi.org\/10.1109\/ICARCV.2014.7064414 . http:\/\/ieeexplore.ieee.org\/document\/7064414\/","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"2117_CR55","unstructured":"Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional lstm network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp 802\u2013810. arXiv:1506.04214"},{"key":"2117_CR56","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5999\u20136009. arXiv:1706.03762"},{"key":"2117_CR57","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training. Homol Homot Appl 9:399\u2013438"},{"key":"2117_CR58","unstructured":"Chollet F, et al (2015) Keras. GitHub. https:\/\/github.com\/fchollet\/keras"},{"key":"2117_CR59","unstructured":"Keras: ConvLSTM2D layer (2022). https:\/\/keras.io\/api\/layers\/recurrent_layers\/conv_lstm2d\/"},{"key":"2117_CR60","unstructured":"Amogh J (2021) Keras: next-frame video prediction with convolutional LSTMs. https:\/\/keras.io\/examples\/vision\/conv_lstm\/https:\/\/github.com\/keras-team\/keras-io\/blob\/master\/examples\/vision\/conv_lstm.py"},{"key":"2117_CR61","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.solener.2021.06.006","volume":"224","author":"A Kunwar","year":"2021","unstructured":"Kunwar A, Malla PB, Sun J, Qu L, Ma H (2021) Convolutional neural network model for synchrotron radiation imaging datasets to automatically detect interfacial microstructure: An in situ process monitoring tool during solar PV ribbon fabrication. Sol Energy 224:230\u2013244. https:\/\/doi.org\/10.1016\/j.solener.2021.06.006","journal-title":"Sol Energy"},{"key":"2117_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3449064","volume":"18","author":"K Um","year":"2021","unstructured":"Um K, Hu X, Wang B, Thuerey N (2021) Spot the difference: accuracy of numerical simulations via the human visual system. ACM Trans Appl Percept 18:1\u201315. https:\/\/doi.org\/10.1145\/3449064","journal-title":"ACM Trans Appl Percept"},{"key":"2117_CR63","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"2117_CR64","doi-asserted-by":"publisher","unstructured":"Silva EA, Panetta K, Agaian SS (2007) Quantifying image similarity using measure of enhancement by entropy. 6579:219\u2013230. https:\/\/doi.org\/10.1117\/12.720087","DOI":"10.1117\/12.720087"},{"issue":"8","key":"2117_CR65","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/0262-8856(92)90076-F","volume":"10","author":"JJ Koenderink","year":"1992","unstructured":"Koenderink JJ, Doorn AJ (1992) Surface shape and curvature scales. Image Vis Comput 10(8):557\u2013564. https:\/\/doi.org\/10.1016\/0262-8856(92)90076-F","journal-title":"Image Vis Comput"},{"key":"2117_CR66","unstructured":"Bradski G (2000) The OpenCV Library. Dr. Dobb\u2019s Journal of Software Tools"},{"key":"2117_CR67","doi-asserted-by":"publisher","first-page":"453","DOI":"10.7717\/peerj.453","volume":"2","author":"S Walt","year":"2014","unstructured":"Walt S, Sch\u00f6nberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T (2014) scikit-image: image processing in python. PeerJ 2:453. https:\/\/doi.org\/10.7717\/peerj.453","journal-title":"PeerJ"},{"key":"2117_CR68","unstructured":"Khalel A (2018) Sewar. https:\/\/github.com\/andrewekhalel\/sewar"},{"key":"2117_CR69","unstructured":"Kumar J, Chen F, Doermann D (2019) Sharpness estimation for document and scene images. https:\/\/github.com\/umang-singhal\/pydom"},{"issue":"3","key":"2117_CR70","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90\u201395. https:\/\/doi.org\/10.1109\/MCSE.2007.55","journal-title":"Comput Sci Eng"},{"key":"2117_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.scriptamat.2024.116027","volume":"244","author":"U Subedi","year":"2024","unstructured":"Subedi U, Moelans N, Ta\u0144ski T, Kunwar A (2024) Rapid portabilization of elasto-chemical evolution data for dental Ti\u2013Cr alloy microstructure through sparsification and tensor computation. Scripta Mater 244:116027. https:\/\/doi.org\/10.1016\/j.scriptamat.2024.116027","journal-title":"Scripta Mater"},{"key":"2117_CR72","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TIP.2011.2173206","volume":"21","author":"D Brunet","year":"2012","unstructured":"Brunet D, Vrscay ER, Wang Z (2012) On the mathematical properties of the structural similarity index. IEEE Trans Image Process 21:1488\u20131499. https:\/\/doi.org\/10.1109\/TIP.2011.2173206","journal-title":"IEEE Trans Image Process"},{"key":"2117_CR73","doi-asserted-by":"publisher","first-page":"453","DOI":"10.7717\/peerj.453","volume":"2","author":"S Walt","year":"2014","unstructured":"Walt S, Schonberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T (2014) scikit-image: image processing in Python. PeerJ 2:453. https:\/\/doi.org\/10.7717\/peerj.453","journal-title":"PeerJ"},{"issue":"11","key":"2117_CR74","doi-asserted-by":"publisher","first-page":"4104","DOI":"10.1118\/1.2358326","volume":"33","author":"DM Catarious","year":"2006","unstructured":"Catarious DM, Baydush AH, Floyd CE (2006) Characterization of difference of Gaussian filters in the detection of mammographic regions. Med Phys 33(11):4104\u20134114. https:\/\/doi.org\/10.1118\/1.2358326","journal-title":"Med Phys"},{"key":"2117_CR75","doi-asserted-by":"publisher","unstructured":"Zhao F, DeSilva CJS (2002) Use of the Laplacian of Gaussian operator in prostate ultrasound image processing 2:812\u2013815. https:\/\/doi.org\/10.1109\/iembs.1998.745557","DOI":"10.1109\/iembs.1998.745557"},{"key":"2117_CR76","unstructured":"Fotin SV, Yankelevitz DF, Henschke CI, Reeves AP (2019) A multiscale Laplacian of Gaussian (LoG) filtering approach to pulmonary nodule detection from whole-lung CT scans. arXiv:1907.08328"},{"issue":"6","key":"2117_CR77","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1109\/TSMCB.2012.2228639","volume":"43","author":"H Kong","year":"2013","unstructured":"Kong H, Akakin HC, Sarma SE (2013) A generalized Laplacian of gaussian filter for blob detection and its applications. IEEE Trans Cybern 43(6):1719\u20131733. https:\/\/doi.org\/10.1109\/TSMCB.2012.2228639","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"2117_CR78","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/S0734-189X(89)80036-2","volume":"48","author":"GE Sotak","year":"1989","unstructured":"Sotak GE, Boyer KL (1989) The Laplacian-of-Gaussian kernel: a formal analysis and design procedure for fast, accurate convolution and full-frame output. Comput Vis Graph Image Process 48(2):147\u2013189. https:\/\/doi.org\/10.1016\/S0734-189X(89)80036-2","journal-title":"Comput Vis Graph Image Process"},{"issue":"1","key":"2117_CR79","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10851-014-0541-0","volume":"52","author":"T Lindeberg","year":"2015","unstructured":"Lindeberg T (2015) Image matching using generalized scale-space interest points. J Math Imaging Vis 52(1):3\u201336. https:\/\/doi.org\/10.1007\/s10851-014-0541-0","journal-title":"J Math Imaging Vis"},{"issue":"8","key":"2117_CR80","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1016\/S0031-3203(98)00163-0","volume":"32","author":"SR Gunn","year":"1999","unstructured":"Gunn SR (1999) On the discrete representation of the Laplacian of Gaussian. Pattern Recogn 32(8):1463\u20131472. https:\/\/doi.org\/10.1016\/S0031-3203(98)00163-0","journal-title":"Pattern Recogn"},{"issue":"1","key":"2117_CR81","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/0734-189X(86)90220-3","volume":"33","author":"PJ Besl","year":"1986","unstructured":"Besl PJ, Jain RC (1986) Invariant surface characteristics for 3D object recognition in range images. Comput Vis Graph Image Process 33(1):33\u201380. https:\/\/doi.org\/10.1016\/0734-189X(86)90220-3","journal-title":"Comput Vis Graph Image Process"},{"key":"2117_CR82","doi-asserted-by":"publisher","unstructured":"Cantzler H, Fisher RB. Comparison of HK and SC curvature description methods. In: Proceedings third international conference on 3-D digital imaging and modeling, pp 285\u2013291. IEEE Computer Society. https:\/\/doi.org\/10.1109\/IM.2001.924458. http:\/\/ieeexplore.ieee.org\/document\/924458\/","DOI":"10.1109\/IM.2001.924458"},{"issue":"10","key":"2117_CR83","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1088\/0953-8984\/9\/10\/007","volume":"9","author":"T Saha","year":"1997","unstructured":"Saha T, Mookerjee A (1997) A phase-stability study of PdRh alloys. J Phys Condens Matter 9(10):2179\u20132186. https:\/\/doi.org\/10.1088\/0953-8984\/9\/10\/007","journal-title":"J Phys Condens Matter"},{"issue":"7","key":"2117_CR84","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.3390\/met12071220","volume":"12","author":"DA Sigala-Garcia","year":"2022","unstructured":"Sigala-Garcia DA, Lopez-Hirata VM, Saucedo-Munoz ML, Dorantes-Rosales HJ, Villegas-Cardenas JD (2022) Phase-field simulation of spinodal decomposition in Mn\u2013Cu alloys. Metals 12(7):1220. https:\/\/doi.org\/10.3390\/met12071220","journal-title":"Metals"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-025-02117-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-025-02117-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-025-02117-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T23:29:07Z","timestamp":1758065347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-025-02117-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,28]]},"references-count":84,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2117"],"URL":"https:\/\/doi.org\/10.1007\/s00366-025-02117-z","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,28]]},"assertion":[{"value":"19 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}