85 citations · 115 across the 10 of their papers we have counts for
10 papers
An Agentic Orchestration of Atomistic Simulations
Rahul Somasundaram, Adela Habib, Khanh Dang +12
Atomistic simulations are central to materials design, but their execution involves complex, multi-step workflows that require significant human expertise. Here, we present an agen…
A Foundation Model for Material Fracture Prediction
Agnese Marcato, Aleksandra Pachalieva, Ryley G. Hill +14
Accurately predicting when and how materials fail is critical to designing safe, reliable structures, mechanical systems, and engineered components that operate under stress. Yet,…
Information Extraction from Historical Well Records Using A Large Language Model
Zhiwei Ma, Javier E. Santo, Greg Lackey +2
To reduce environmental risks and impacts from orphaned wells (abandoned oil and gas wells), it is essential to first locate and then plug these wells. Although some historical doc…
Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations
Alexander C. Murph, Justin D. Strait, Kelly R. Moran +3
Large-scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub-surface transport of particles since direct observation of real world undergr…
Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration
Aleksandra Pachalieva, Jeffrey D. Hyman, Daniel O'Malley +2
We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO mineralization req…
Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization
Agnese Marcato, Daniel O'Malley, Hari Viswanathan +2
Recreating complex, high-dimensional global fields from limited data points is a grand challenge across various scientific and industrial domains. Given the prohibitive costs of sp…
Learning a General Model of Single Phase Flow in Complex 3D Porous Media
Javier E. Santos, Agnese Marcato, Qinjun Kang +4
Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures an…
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer
Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi +2
Data-driven modeling can suffer from a constant demand for data, leading to reduced accuracy and impractical for engineering applications due to the high cost and scarcity of infor…
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties
T. Kadeethum, D. O'Malley, Y. Choi +3
Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In…
Modeling flow and transport in fracture networks using graphs
S. Karra, D. O'Malley, J. D. Hyman +2
Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital fo…