144 citations · 635 across the 40 of their papers we have counts for
10 papers · 1 filter
Data-informed Emulators for Multi-Physics Simulations
Hannah Lu, Dinara Ermakova, Haruko Murakami Wainwright +2
Machine learning techniques are powerful tools for construction of emulators for complex systems. We explore different machine learning methods and conceptual methodologies, rangin…
Autonomous learning of nonlocal stochastic neuron dynamics
Tyler E. Maltba, Hongli Zhao, Daniel M. Tartakovsky
Neuronal dynamics is driven by externally imposed or internally generated random excitations/noise, and is often described by systems of random or stochastic ordinary differential…
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…
GINNs: Graph-Informed Neural Networks for Multiscale Physics
Eric J. Hall, Søren Taverniers, Markos A. Katsoulakis +1
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate fo…
Extended Dynamic Mode Decomposition for Inhomogeneous Problems
Hannah Lu, Daniel M. Tartakovsky
Dynamic mode decomposition (DMD) is a powerful data-driven technique for construction of reduced-order models of complex dynamical systems. Multiple numerical tests have demonstrat…
Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks
Hannah Lu, Daniel M. Tartakovsky
Construction of reduced-order models (ROMs) for hyperbolic conservation laws is notoriously challenging mainly due to the translational property and nonlinearity of the governing e…