12 citations · 17 across the 8 of their papers we have counts for
6 papers · 1 filter
Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
Adrienne M. Propp, Mauro Perego, Eric C. Cyr +5
Accurate yet efficient surrogate models are essential for large-scale simulations of partial differential equations (PDEs), particularly for uncertainty quantification (UQ) tasks t…
CODE: A global approach to ODE dynamics learning
Nils Wildt, Daniel M. Tartakovsky, Sergey Oladyshkin +1
Ordinary differential equations (ODEs) are a conventional way to describe the observed dynamics of physical systems. Scientists typically hypothesize about dynamical behavior, prop…
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs
Adrienne M. Propp, Jonas A. Actor, Elise Walker +3
Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfa…
Baseflow identification via explainable AI with Kolmogorov-Arnold networks
Chuyang Liu, Tirthankar Roy, Daniel M. Tartakovsky +1
Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov-Arnold networks (KANs), a clas…
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
Adrienne M. Propp, Daniel M. Tartakovsky
The development of efficient surrogates for partial differential equations (PDEs) is a critical step towards scalable modeling of complex, multiscale systems-of-systems. Convolutio…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…