| Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management A Pachalieva, D O’Malley, DR Harp, H Viswanathan Scientific Reports 12 (1), 18734, 2022 | 19 | 2022 |
| Validity of the molecular-dynamics-lattice-gas global equilibrium distribution function MR Parsa, A Pachalieva, AJ Wagner International Journal of Modern Physics C 30 (10), 1941007, 2019 | 8 | 2019 |
| Connecting lattice Boltzmann methods to physical reality by coarse-graining Molecular Dynamics simulations A Pachalieva, AJ Wagner arXiv preprint arXiv:2109.05009, 2021 | 4 | 2021 |
| Non-Gaussian distribution of displacements for Lennard-Jones particles in equilibrium A Pachalieva, AJ Wagner Physical Review E 102 (5), 053310, 2020 | 4 | 2020 |
| Molecular dynamics lattice gas equilibrium distribution function for Lennard–Jones particles A Pachalieva, AJ Wagner Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2021 | 2 | 2021 |
| DPFEHM: a differentiable subsurface physics simulator D O'Malley, SY Greer, A Pachalieva, W Hao, D Harp, VV Vesselinov Journal of Open Source Software 8 (90), 4560, 2023 | 1 | 2023 |
| Impact of artificial topological changes on flow and transport through fractured media due to mesh resolution AA Pachalieva, MR Sweeney, H Viswanathan, E Stein, R Leone, ... Computational Geosciences 27, 1145–1163, 2023 | 1 | 2023 |
| Prediction and uncertainty quantification of shale well performance using multifidelity Monte Carlo M Mehana, A Pachalieva, A Kumar, J Santos, D O'Malley, W Carey, ... Gas Science and Engineering 110, 204877, 2023 | 1 | 2023 |
| GLUE code: A framework handling communication and interfaces between scales A Pachalieva, RS Pavel, JE Santos, A Diaw, N Lubbers, M Mehana, ... Journal of Open Source Software 7 (80), 4822, 2022 | 1 | 2022 |
| Dynamically adaptive 2.5 d porous media flow simulation on xeon phi architectures A Pachalieva | 1 | 2016 |
| Derivation of lattice Boltzmann from coarse graining Molecular Dynamics and lattice gases A Wagner, A Pachalieva, N Seekins Bulletin of the American Physical Society, 2024 | | 2024 |
| Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration A Pachalieva, JD Hyman, D O'Malley, H Viswanathan, G Srinivasan arXiv preprint arXiv:2312.13451, 2023 | | 2023 |
| Transitioning from advection to diffusion dominated particle transport due to matrix diffusion with finite block size in fractured media S Volponi, J Hyman, D Bolster, A Pachalieva, MR Sweeney AGU23, 2023 | | 2023 |
| Machine Learning for Probabilistic Model with Random Coefficients NW Hengartner, F Hickernell, A Sorokin, D O'Malley, JM Hyman, ... Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2023 | | 2023 |
| Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media AG Sorokin, A Pachalieva, D O'Malley, JM Hyman, FJ Hickernell, ... arXiv preprint arXiv:2310.13765, 2023 | | 2023 |
| Predictive scale-bridging simulations through active learning S Karra, M Mehana, N Lubbers, Y Chen, A Diaw, JE Santos, A Pachalieva, ... Scientific Reports 13 (1), 16262, 2023 | | 2023 |
| From Molecular Dynamics to lattice Boltzmann A Wagner APS March Meeting Abstracts 2023, G62. 011, 2023 | | 2023 |
| Assessing the influence of matrix diffusion with finite block size on particle transport in fractured media S Volponi, D Bolster, A Pachalieva, MR Sweeney, J Hyman AGU Fall Meeting Abstracts 2022, H52K-0595, 2022 | | 2022 |
| Molecular Dynamics Lattice Gas Analysis Tool AA Pachalieva Technische Universität München, 2022 | | 2022 |
| Scale-bridging using machine-learning: nanoconfinement effects in porous media H Viswanathan, JE Santos, N Lubbers, A Pachalieva, M Mehana, Q Kang, ... AGU Fall Meeting Abstracts 2021, H12E-02, 2021 | | 2021 |
Hari ViswanathanSenior Scientist, Los Alamos National LaboratoryVerified email at lanl.gov
