Gowri Srinivasan, Ph.D.
Affiliations: | 2008 | University of New Mexico, Albuquerque, NM, United States |
Area:
MathematicsGoogle:
"Gowri Srinivasan"Parents
Sign in to add mentor| Alejandro B. Aceves | grad student | 2008 | Univ. of New Mexico | |
| (Uncertainty quantification in stochastic processes.) | ||||
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Publications
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| Fernández-Godino MG, Panda N, O’Malley D, et al. (2021) Accelerating high-strain continuum-scale brittle fracture simulations with machine learning Computational Materials Science. 186: 109959 |
| Osthus D, Hyman JD, Karra S, et al. (2020) A Probabilistic Clustering Approach for Identifying Primary Subnetworks of Discrete Fracture Networks with Quantified Uncertainty Siam/Asa Journal On Uncertainty Quantification. 8: 573-600 |
| Dana S, Srinivasan S, Karra S, et al. (2020) Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks Journal of Petroleum Science and Engineering. 107791 |
| Larkin K, Rougier E, Chau V, et al. (2020) Scale bridging damage model for quasi-brittle metals informed with crack evolution statistics Journal of the Mechanics and Physics of Solids. 138: 103921 |
| Panda N, Osthus D, Srinivasan G, et al. (2020) Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling Journal of Computational Physics. 420: 109719 |
| Srinivasan S, Cawi E, Hyman J, et al. (2020) Physics-informed machine learning for backbone identification in discrete fracture networks Computational Geosciences. 24: 1429-1444 |
| Sherman T, Hyman JD, Bolster D, et al. (2019) Characterizing the impact of particle behavior at fracture intersections in three-dimensional discrete fracture networks. Physical Review. E. 99: 013110 |
| Mudunuru MK, Panda N, Karra S, et al. (2019) Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials Applied Sciences. 9: 2706 |
| Schwarzer M, Rogan B, Ruan Y, et al. (2019) Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks Computational Materials Science. 162: 322-332 |
| Hunter A, Moore BA, Mudunuru M, et al. (2019) Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications Computational Materials Science. 157: 87-98 |