Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management. - Aleksandra Pachalieva; Daniel O'Malley; Dylan Robert Harp; Hari S. Viswanathan (CoRR, 2022)

Predictive Scale-Bridging Simulations through Active Learning. - Satish Karra; Mohammed Mehana; Nicholas Lubbers; Yu Chen; Abdourahmane Diaw; Javier E. Santos; Aleksandra Pachalieva; Robert S. Pavel; Jeffrey R. Haack; Michael M. McKerns; Christoph Junghans; Qinjun Kang; et al. (CoRR, 2022)

GLUE Code: A framework handling communication and interfaces between scales. - Aleksandra Pachalieva; Robert S. Pavel; Javier E. Santos; Abdourahmane Diaw; Nicholas Lubbers; Mohammed Mehana; Jeffrey R. Haack; Hari S. Viswanathan; Daniel Livescu; Timothy C. Germann; Christoph Junghans (J. Open Source Softw., 2022)

Impact of artificial topological changes on flow and transport through fractured media due to mesh resolution. - Aleksandra A. Pachalieva; Matthew R. Sweeney; Hari S. Viswanathan; Emily Stein; Rosie Leone; Jeffrey D. Hyman (CoRR, 2023)

Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media. - Aleksei G. Sorokin; Aleksandra Pachalieva; Daniel O'Malley; James Mac Hyman; Fred J. Hickernell; Nicolas W. Hengartner (CoRR, 2023)

Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration. - Aleksandra Pachalieva; Jeffrey D. Hyman; Daniel O'Malley; Hari S. Viswanathan; Gowri Srinivasan (CoRR, 2023)

DPFEHM: a differentiable subsurface physics simulator. - Daniel O'Malley; Sarah Y. Greer; Aleksandra Pachalieva; Wu Hao; Dylan Robert Harp; Velimir V. Vesselinov (J. Open Source Softw., 2023)

Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks. - Emily Shinkle; Aleksandra Pachalieva; Riti Bahl; Sakib Matin; Brendan Gifford; Galen T. Craven; Nicholas Lubbers (CoRR, 2024)

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction. - Paul Schwerdtner; Prakash Mohan 004; Aleksandra Pachalieva; Julie Bessac; Daniel O'Malley; Benjamin Peherstorfer (CoRR, 2024)

Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials. - Sakib Matin; Emily Shinkle; Yulia Pimonova; Galen T. Craven; Aleksandra Pachalieva; Ying Wai Li; Kipton Barros; Nicholas Lubbers (CoRR, 2025)

A Foundation Model for Material Fracture Prediction. - Agnese Marcato; Aleksandra Pachalieva; Ryley G. Hill; Kai Gao; Xiaoyu Wang; Esteban Rougier; Zhou Lei; Vinamra Agrawal; Janel Chua; Qinjun Kang; Jeffrey D. Hyman; Abigail Hunter; et al. (CoRR, 2025)

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management. - Harun Ur Rashid; Aleksandra Pachalieva; Daniel O'Malley (CoRR, 2025)