UW Data Science Seminar with Megan Ebers

Title slide from the UW eScience Data Science seminar that says "Mobile sensing with shallow recurrent decoder networks. Megan R. Ebers"

Steele lab member and postdoctoral scholar, Megan Ebers, was featured in the Winter 2024 UW Data Science Seminar series. You can watch her full presentation on “Mobile sensing with shallow recurrent decoder networks” linked HERE on UW eScience Institute’s YouTube channel.

Abstract: Sensing is a fundamental task for the monitoring, forecasting, and control of complex systems. In many applications, a limited number of sensors are available and must move with the dynamics, such as with wearable technology or ocean monitoring buoys. In these dynamic systems, the sensors’ time history encodes a significant amount of information that can be extracted for critical tasks. We show that by leveraging the time-history of a sparse set of sensors, we can encode global information of the measured high-dimensional system using shallow recurrent decoder networks. This paradigm has important applications for technical challenges in climate modeling, natural disaster evaluation, and personalized health monitoring; we focus especially on how this paradigm has the potential to transform the way we monitor and manage movement-related health outcomes.

Bio: Megan Ebers is a postdoctoral scholar in applied mathematics with UW’s NSF AI Institute in Dynamic Systems. In her PhD research, she developed and applied machine learning methods for dynamics systems to understand and enable human mobility. Her postdoctoral research focuses on data-driven and reduced-order methods for complex systems, so as to continue her work in human-centered research challenges, as well as to extend her research to a broader set of technical challenges, including turbulent flow modeling, natural disaster monitoring, and acoustic object detection.

Charlotte Caskey on “Gears of Progress” Podcast

Gears of Progress Episode Three featured Charlotte Caskey on spinal stimulation in children with cerebral Palsy, fancy neuroscience, and balance between clinical research and real world. Charlotte has long brown hair. She is wearing glasses and a cozy scarf.

“Gears of Progress” Episode Three featured Charlotte Caskey on spinal stimulation in children with cerebral Palsy, fancy neuroscience, and balance between clinical research and real world.

Gears of Progress Logo with three gears featuring assistive devicesName: Gears of Progress

PlatformsSpotifyApple PodcastsAmazon MusicCastbox

Release frequency: bi-weekly on Fridays

Theme: Podcast about research and innovations in rehabilitation engineering and assistive technologies aimed to improve accessibility for people with disabilities. Every episode will feature engineers, medical professionals, end-users, and organizations who focus on improving the health and well-being of individuals with disabilities. We will be covering topics such as emerging tech, outcome measures, medical practice, public policy, accessibility education, and so much more!

Twitterhttps://twitter.com/GearsOfProgress

Mia Hoffman on “Gears of Progress” Podcast

Gears of Progress Episode Two featuring Mia Hoffman on early childhood mobility, young kids as participants, and accessibility of research for people with disabilities.

“Gears of Progress” Episode Two featured Mia Hoffman on early childhood mobility, young kids as participants, and accessibility of research for people with disabilities.

Gears of Progress Logo with three gears featuring assistive devices

Name: Gears of Progress

Platforms: Spotify, Apple Podcasts, Amazon Music, Castbox

Release frequency: bi-weekly on Fridays

Theme: Podcast about research and innovations in rehabilitation engineering and assistive technologies aimed to improve accessibility for people with disabilities. Every episode will feature engineers, medical professionals, end-users, and organizations who focus on improving the health and well-being of individuals with disabilities. We will be covering topics such as emerging tech, outcome measures, medical practice, public policy, accessibility education, and so much more!

Twitterhttps://twitter.com/GearsOfProgress

Steele Lab presents at CREATE Research Showcase

A group of Steele Lab members posing for a photo

The Center for Research and Education on Accessible Technology and Experiences (CREATE) hosted a Research Showcase and Holiday party on December 12, 2023. CREATE’s mission is to make technology accessible and to make the world accessible through technology.

Steele Lab members, Kate, Victoria (Tori), and Charlotte presented posters at the CREATE Research Showcase to highlight design, development & research of tech to support individuals with disabilities.

Way to go, team!

MR Ebers, KM Steele, JN Kutz (2024) “Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects”

Journal Article in SIAM Journal on Applied Dynamical Systems

Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of the system, revealing the approximate nature of the equations and/or the signal-to-noise ratio of the sensor itself. In modern dynamical systems, such discrepancies between model and measurement can lead to poor quantification, often undermining the ability to produce accurate and precise control algorithms.

Top panel: An approximate dynamical model f(·) provides estimates of system behavior used for both reconstruction and forecasting (shaded region), x(t). However, true behavior x0(t) (without observation noise) deviates from these estimates. The goal of discrepancy modeling is to learn a discrepancy model that recovers the missing physics and augments the approximate dynamics to improve system characterization, ˜x(t). Bottom panel: There are two approaches for building a discrepancy model to estimate missing physics: (i) modeling systematic state-space residual between the approximate state space, x(t), and true state space, x0(t), and (ii) learning the deterministic dynamical error between the true dynamics, x˙ 0(t) = f(x0(t)) + g(x0(t)), and the approximate dynamics, x˙ (t) = f(x(t)). In real-world systems, the true system behavior is noisily observed, yk = x0(tk) + N(μ, σ), model-measurement mismatch contains both deterministic and random effects; measurements yk = y(kΔt) denote a continuous dynamical system’s full state noisily observed at discrete time points.

Aim: Introduce a discrepancy modeling framework to identify the missing physics and resolve the model-measurement mismatch with two distinct approaches: (i) by learning a model for the evolution of systematic state-space residual, and (ii) by discovering a model for the deterministic dynamical error. Regardless of approach, a common suite of data-driven model discovery methods can be used.

Method: Specifically, we use four fundamentally different methods to demonstrate the mathematical implementations of discrepancy modeling: (i) the sparse identification of nonlinear dynamics (SINDy), (ii) dynamic mode decomposition (DMD), (iii) Gaussian process regression (GPR), and (iv) neural networks (NN). The choice of method depends on one’s intent (e.g., mechanistic interpretability) for discrepancy modeling, sensor measurement characteristics (e.g., quantity, quality, resolution), and constraints imposed by practical applications (e.g., state- or dynamical-space operability).

Results: We demonstrate the utility and suitability for both discrepancy modeling approaches using the suite of data-driven modeling methods on three continuous dynamical systems under varying signal-to-noise ratios. Finally, we emphasize structural shortcomings of each discrepancy modeling approach depending on error type.

Interpretation: In summary, if the true dynamics are unknown (i.e., an imperfect model), one should learn a discrepancy model of the missing physics in the dynamical space. Yet, if the true dynamics are known yet model-measurement mismatch still exists, one should learn a discrepancy model in the state space.