# Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials
## 3 An Algorithm to Estimate Failure Paths
Lastly, our method can be coupled with other machine learning and graph-based methods [92; 93; 94; 95] for increased accuracy of failure paths prediction, which is our future work. Conceptualization - M.K.M. ; methodology - M.K.M. and N.P. ; software - M.K.M., N.P., V.T.C., and E.R. ; validation - M.K.M. and N.P. ; formal analysis - M.K.M. and N.P. ; investigation - M.K.M., N.P., S.K., G.S., V.T.C., E.R., A.H., and H.S.V. ; resources - M.K.M., N.P., S.K., G.S., V.T.C., E.R., A.H., and H.S.V. ; data curation - M.K.M., N.P., V.T.C., and E.R. ; writing-original draft preparation - M.K.M. ; writing-review and editing, M.K.M., N.P., S.K., G.S., V.T.C., E.R., A.H., and H.S.V. ; visualization - [PERSON] and N.P. ; supervision - S.K., G.S., A.H., and H.S.V. ; project administration - S.K., G.S., A.H., and H.S.V. ; funding acquisition - M.K.M., S.K., G.S., A.H., and H.S.V. The authors thank the support of the LANL Laboratory Directed Research and Development Directed Research Award 20170103 DR. MKM and SK authors also thank the support of the LANL Laboratory Directed Research and Development Early Career Award 20150693 ECR. MKM gratefully acknowledges the support of LANL Chick-Keller Postdoctoral Fellowship through Center for Space and Earth Sciences (CSES). Authors thank the LANL Institutional Computing program for their support in generating data used in this work. The authors also thank [PERSON] for providing HOSS simulation datasets, which is available at [[https://gitlab.com/LANL-ist-dr-graphs/DR_competition_paper](https://gitlab.com/LANL-ist-dr-graphs/DR_competition_paper)]([https://gitlab.com/LANL-ist-dr-graphs/DR_competition_paper](https://gitlab.com/LANL-ist-dr-graphs/DR_competition_paper)). Additional information regarding the simulation datasets can be obtained from the corresponding author. The authors declare no conflict of interest. ## References

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