activity
20222025
most citedDiscrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects

11 citations · 14 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SY2025

First Contact: Data-driven Friction-Stir Process Control

James Koch, Ethan King, WoongJo Choi +4

This study validates the use of Neural Lumped Parameter Differential Equations for open-loop setpoint control of the plunge sequence in Friction Stir Processing (FSP). The approach…

physics.flu-dyn2025

Coarse graining and reduced order models for plume ejection dynamics

Ike Griss Salas, Megan R. Ebers, Jake Stevens-Haas +1

Monitoring the atmospheric dispersion of pollutants is increasingly critical for environmental impact assessments. High-fidelity computational models are often employed to simulate…

cs.LG2023★ 3 cited

Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction

Megan R. Ebers, Jan P. Williams, Katherine M. Steele +1

Sensing is one of the most fundamental tasks for the monitoring, forecasting and control of complex, spatio-temporal systems. In many applications, a limited number of sensors are…

nlin.AO2022

Universal Dynamics of Damped-Driven Systems: The Logistic Map as a Normal Form for Energy Balance

J. Nathan Kutz, Aminur Rahman, Megan R. Ebers +2

Damped-driven systems are ubiquitous in engineering and science. Despite the diversity of physical processes observed in a broad range of applications, the underlying instabilities…

stat.ML2022★ 11 cited

Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects

Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz

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 accurac…