68 citations · 190 across the 16 of their papers we have counts for
16 papers
Time-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environments
Erik Jagnandan, Mulugeta Haile, Gregory Barber +1
Dynamic obstacle avoidance in unstructured outdoor environments remains a critical challenge for autonomous mobile robots, particularly when large-scale robot-specific training dat…
Generating Physically Sound Designs from Text and a Set of Physical Constraints
Gregory Barber, Todd C. Henry, Mulugeta A. Haile
We present TIDES, a text informed design approach for generating physically sound designs based on a textual description and a set of physical constraints. TIDES jointly optimizes…
Model-free tracking control of complex dynamical trajectories with machine learning
Zheng-Meng Zhai, Mohammadamin Moradi, Ling-Wei Kong +3
Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control…
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…
A Model for Multi-Agent Heterogeneous Interaction Problems
Christopher D. Hsu, Mulugeta A. Haile, Pratik Chaudhari
We introduce a model for multi-agent interaction problems to understand how a heterogeneous team of agents should organize its resources to tackle a heterogeneous team of attackers…
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…
Implicit Particle Filtering via a Bank of Nonlinear Kalman Filters
Iman Askari, Mulugeta A. Haile, Xuemin Tu +1
The implicit particle filter seeks to mitigate particle degeneracy by identifying particles in the target distribution's high-probability regions. This study is motivated by the ne…
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…
SSUE: Simultaneous State and Uncertainty Estimation for Dynamical Systems
Hang Geng, Mulugeta A. Haile, Huazhen Fang
Parameters of the mathematical model describing many practical dynamical systems are prone to vary due to aging or renewal, wear and tear, as well as changes in environmental or se…
Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
Levi McClenny, Mulugeta Haile, Vahid Attari +3
In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc. This task can be furth…
Robust Extended Kalman Filtering for Systems with Measurement Outliers
Huazhen Fang, Mulugeta A. Haile, Yebin Wang
Outliers can contaminate the measurement process of many nonlinear systems, which can be caused by sensor errors, model uncertainties, change in ambient environment, data loss or m…
Nonlinear Bayesian Estimation: From Kalman Filtering to a Broader Horizon
Huazhen Fang, Ning Tian, Yebin Wang +2
This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of eng…