Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis. - Anindya Bhaduri; Yanyan He; Michael D. Shields; Lori Graham-Brady; Robert M. Kirby (J. Comput. Phys., 2018)

An efficient optimization based microstructure reconstruction approach with multiple loss functions. - Anindya Bhaduri; Ashwini Gupta; Audrey Olivier; Lori Graham-Brady (CoRR, 2021)

Stress field prediction in fiber-reinforced composite materials using a deep learning approach. - Anindya Bhaduri; Ashwini Gupta; Lori Graham-Brady (CoRR, 2021)

Machine Learning in Heterogeneous Porous Materials. - Marta D'Elia; Hang Deng; Cedric G. Fraces; Krishna C. Garikipati; Lori Graham-Brady; Amanda A. Howard; George Em Karniadakis; Vahid Keshavarzzadeh; Robert M. Kirby; J. Nathan Kutz; Chunhui Li; Xing Liu; et al. (CoRR, 2022)

Bayesian neural networks for predicting uncertainty in full-field material response. - George D. Pasparakis; Lori Graham-Brady; Michael D. Shields (CoRR, 2024)

Review of the concept of variability response function and its application in stochastic systems. - George Deodatis; Sanjay Arwade; Lori Graham-Brady; Kirubel Teferra (Reliab. Eng. Syst. Saf., 2025)

Efficient Training of Deep Neural Operator Networks via Randomized Sampling. - Sharmila Karumuri; Lori Graham-Brady; Somdatta Goswami (World Sci. Annu. Rev. Artif. Intell., 2025)

Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems. - Sharmila Karumuri; Lori Graham-Brady; Somdatta Goswami (CoRR, 2025)

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs. - Ishan Khurjekar; Indrashish Saha; Lori Graham-Brady; Somdatta Goswami (CoRR, 2025)