Aleksandra Pachalieva

According to our database, Aleksandra Pachalieva authored at least 9 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction.

Paul Schwerdtner, Prakash Mohan, Aleksandra Pachalieva, Julie Bessac, Daniel O'Malley, Benjamin Peherstorfer

CoRR, 2024

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

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., October, 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

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 M. Hyman, Fred J. Hickernell, Nicolas W. Hengartner

CoRR, 2023

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

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., December, 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, Daniel Livescu, Timothy C. Germann, Hari S. Viswanathan

CoRR, 2022

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