[2024 Nov] I presented at the AAAI FSS for Integrated Computational Approaches for Scientific Discovery.
[2024 Nov] Our work on automated scientific discovery in dynamics was released.
[2023 Jul] Our paper was accepted to the Journal of Sound and Vibration
[2023 Apr] Successfully defended my dissertation proposal
[2022 Oct] Joined the General Robotics Lab @ Duke
[2022 Apr] Our paper was accepted to Communications in Nonlinear Science and Numerical Simulation
[2022 Apr] Passed my qualifying exam
[2022 Mar] Awarded the NSF Graduate Research Fellowship
[2022 Feb] Presented our work
at the International Modal Analysis Conference
[2021 Sept] Our paper was accepted in the European Journal of Physics
[2020 Aug] Joined the Dynamical Systems Lab @ Duke
[2019 Dec] Graduated from the University of Kentucky with honors
Research
Part of my research centers on developing integrated computational frameworks for scientific discovery in dynamical systems, robotics, and control.
I also focus on designing adaptable, whole-body-aware robots that leverage deep learning to achieve robust performance in complex, real-world environments.
Automated Global Analysis of Experimental Dynamics through Low-Dimensional Linear Embeddings Sam A. Moore ,
Brian P. Mann,
Boyuan Chen
Arxiv Preprint paperproject pagecodeyoutube
This work presents an end-to-end approach for discovering hidden stuctures in experimental data from dynamical systems. We simulaneously address challenges in applied dynamical systems due to mathematical modelling, nonlinearity, and high dimensionality through low-dimensional linear embeddings of nonlinear dynamics.
Stability Prediction via Parameter Estimation from Milling Time Series
James D. Turner,
Sam A. Moore,
Brian P. Mann
Journal of Sound and Vibration, 2023 paper
This paper introduces an approach to automated milling stability analysis whereby the vibration behavior of a milling tool during
cutting is used to directly obtain a bifurcation diagram of the system at real-time rates.
A Model-Free Sampling Method for Basins of Attraction Using Hybrid Active Learning
Xue-She Wang,
Sam A. Moore,
James D. Turner,
Brian P. Mann
Communications in Nonlinear Science and Numerical Simulation. 2022 paper project page
This work explores hybrid active learning as an efficient method to automatically discover the basins of attraction of dynamical systems.
Supervised Learning for Abrupt Change Detection in a Driven Eccentric Wheel
Sam A. Moore,
Dean Culver,
Brian P. Mann
International Modal Analysis Conference, 2021
paper
This paper seeks to explore and compare supervised learning methods for phase identification
(i.e., roll, slip, and hop) in simulated data from a driven eccentric wheel as a prototypical non-smooth system.
The Eccentric Disk and Its Eccentric behavior
Sam A. Moore,
Dean Culver,
Brian P. Mann
European Journal of Physics, 2021
paper
An eccentric disk has a non-constant normal force and therefore has four distinct phases of motion: oscillations about a stable equilibrium, roll without slip, roll with slip, and hop.
In this work, the system is analytically modeled using an augmented Lagrangian formulation, solved with numerical integration, and experimentally realized.