activity
20132022
most citedMitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

math.OC2022

Estimating Driver Response Rates to Variable Message Signage at Seattle-Tacoma International Airport

Soumya Vasisht, Shushman Choudhury, Nawaf Nazir +2

We apply Bayesian Linear Regression to estimate the response rate of drivers to variable message signs at Seattle-Tacoma International Airport, or SeaTac. Our approach uses vehicle…

math.OC2022★ 1 cited

Mitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

Nawaf Nazir, Soumya Vasisht, Shushman Choudhury +2

We present a data-driven control framework for adaptively managing landside congestion at airports. Ground traffic significantly impacts airport operations and critical efficiency,…

math.DS2022

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles

Wenceslao Shaw Cortez, Soumya Vasisht, Aaron Tuor +2

Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely d…

eess.SY2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

Jan Drgona, Aaron Tuor, Soumya Vasisht +2

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multipara…

math.DS2021

Constrained Block Nonlinear Neural Dynamical Models

Elliott Skomski, Soumya Vasisht, Colby Wight +3

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for…

cs.LG2020

Dissipative Deep Neural Dynamical Systems

Jan Drgona, Soumya Vasisht, Aaron Tuor +1

In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks. We leverag…

astro-ph.EP2013

Mission to the Trojan Asteroids: lessons learned during a JPL Planetary Science Summer School mission design exercise

Serina Diniega, Kunio M. Sayanagi, Jeffrey Balcerski +16

The 2013 Planetary Science Decadal Survey identified a detailed investigation of the Trojan asteroids occupying Jupiter's L4 and L5 Lagrange points as a priority for future NASA mi…