144 citations · 635 across the 40 of their papers we have counts for
4 papers · 1 filter
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…
Liouville models of particle-laden flow
Daniel Domínguez-Vázquez, Gustaaf B. Jacobs, Daniel M. Tartakovsky
Langevin (stochastic differential) equations are routinely used to describe particle-laden flows. They predict Gaussian probability density functions (PDFs) of a particle's traject…
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…