68 citations · 190 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Tomography of time-dependent quantum spin networks with machine learning
Chen-Di Han, Bryan Glaz, Mulugeta Haile +1
Interacting spin networks are fundamental to quantum computing. Data-based tomography of time-independent spin networks has been achieved, but an open challenge is to ascertain the…
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…