1 citations · 1 across the 2 of their papers we have counts for
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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…
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…
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…
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…
High-Precision Geosteering via Reinforcement Learning and Particle Filters
Ressi Bonti Muhammad, Apoorv Srivastava, Sergey Alyaev +2
Geosteering, a key component of drilling operations, traditionally involves manual interpretation of various data sources such as well-log data. This introduces subjective biases a…