27 citations · 42 across the 6 of their papers we have counts for
7 papers
Digital twins of nonlinear dynamical systems
Ling-Wei Kong, Yang Weng, Bryan Glaz +2
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health…
Underwater Acoustic Communication Channel Modeling using Reservoir Computing
Oluwaseyi Onasami, Ming Feng, Hao Xu +2
Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communicati…
Physical Constraint Embedded Neural Networks for inference and noise regulation
Gregory Barber, Mulugeta A. Haile, Tzikang Chen
Neural networks often require large amounts of data to generalize and can be ill-suited for modeling small and noisy experimental datasets. Standard network architectures trained o…
TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks
Levi D. McClenny, Mulugeta A. Haile, Ulisses M. Braga-Neto
Physics-Informed Neural Networks promise to revolutionize science and engineering practice, by introducing domain-aware deep machine learning models into scientific computation. Se…
Joint Parameter Discovery and Generative Modeling of Dynamic Systems
Gregory Barber, Mulugeta A. Haile, Tzikang Chen
Given an unknown dynamic system such as a coupled harmonic oscillator with springs and point masses. We are often interested in gaining insights into its physical parameters, i…
Adaptable Hamiltonian neural networks
Chen-Di Han, Bryan Glaz, Mulugeta Haile +1
The rapid growth of research in exploiting machine learning to predict chaotic systems has revived a recent interest in Hamiltonian Neural Networks (HNNs) with physical constraints…