144 citations · 635 across the 40 of their papers we have counts for
12 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…
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…
Neural oscillators for magnetic hysteresis modeling
Abhishek Chandra, Taniya Kapoor, Bram Daniels +4
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We d…
Neural oscillators for generalization of physics-informed machine learning
Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky +3
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented…
Discovery of sparse hysteresis models for piezoelectric materials
Abhishek Chandra, Bram Daniels, Mitrofan Curti +3
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression tec…
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…
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky
Identifying the heterogeneous conductivity field and reconstructing the contaminant release history are key aspects of subsurface remediation. Achieving these two goals with limite…
Mutual Information for Explainable Deep Learning of Multiscale Systems
Søren Taverniers, Eric J. Hall, Markos A. Katsoulakis +1
Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping. The latter typically i…