The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Key computational modeling issues in Integrated Computational Materials Engineering
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Citations
Cites
Micromechanics of defects in solids
Mura, T. (1987) · Mechanics of Elastic and Inelastic Solids · doi:10.1007/978-94-009-3489-4 · Cited by 5,539
Bayesian calibration of computer models
Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222
On the quantitative definition of risk
Kaplan, S., & Garrick, B. J. (1981) · Risk Analysis · doi:10.1111/j.1539-6924.1981.tb01350.x · Cited by 3,103
Phase-Field Models for Microstructure Evolution
Chen, L.-Q. (2002) · Annual Review of Materials Research · doi:10.1146/annurev.matsci.32.112001.132041 · Cited by 2,989
Random Heterogeneous Materials
Torquato, S. (2002) · Interdisciplinary Applied Mathematics · doi:10.1007/978-1-4757-6355-3 · Cited by 2,778
Materials Selection in Mechanical Design
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-25539-5 · Cited by 2,679
Milton, G. W. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511613357 · Cited by 2,230
Computational modelling of impact damage in brittle materials
Camacho, G., & Ortíz, M. (1996) · International Journal of Solids and Structures · doi:10.1016/0020-7683(95)00255-3 · Cited by 2,037
Texture Analysis in Materials Science
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Bendsøe, M. P., & Sigmund, O. (2004) · Springer eBooks · doi:10.1007/978-3-662-05086-6 · Cited by 1,727
Cited by
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Roters, F., Diehl, M., Shanthraj, P., Eisenlohr, P., Reuber, C., Wong, S. L., Maiti, T., Ebrahimi, A., Hochrainer, T., Fabritius, H.-O., et al. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2018.04.030 · Cited by 827
Opportunities and Challenges for Machine Learning in Materials Science
Morgan, D., & Jacobs, R. (2020) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010015 · Cited by 549
A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials
Matouš, K., Geers, M. G. D., Kouznetsova, V. G., & Gillman, A. (2017) · Journal of Computational Physics · doi:10.1016/j.jcp.2016.10.070 · Cited by 493
Autonomy in materials research: a case study in carbon nanotube growth
Nikolaev, P., Hooper, D., Webber, F., Rao, R., Decker, K., Krein, M., Poleski, J., Barto, R., & Maruyama, B. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.31 · Cited by 379
Metadata
- Authors
- Panchal, J. H., Kalidindi, S. R., & McDowell, D. L.
- Venue
- Computer-Aided Design
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 45
- Issue
- 1
- Pages
- 4-25
- ISSN
- 0010-4485 (ISSN-L)
- OpenAlex
- W2087292325
- Cited by
- 362
- References
- 97
- FWCI
- 6.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Key computational modeling issues in Integrated Computational Materials Engineering. https://app.cochat.ai/papers/10.1016/j.cad.2012.06.006
- The paper
- Panchal, J. H., Kalidindi, S. R., & McDowell, D. L. (2013). Key computational modeling issues in Integrated Computational Materials Engineering. Computer-Aided Design. https://doi.org/10.1016/j.cad.2012.06.006
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
