activity
20152026
most citedInput Convex Neural Networks for Building MPC

33 citations · 42 across the 7 of their papers we have counts for

collaborators

12 papers

math.OC2026

Optimizing Weighted Hodge Laplacian Flows on Simplicial Complexes

Mathias Hudoba de Badyn, Tyler Summers

Simplicial complexes are generalizations of graphs that describe higher-order network interactions among nodes in the graph. Network dynamics described by graph Laplacian flows hav…

eess.SY2025

Gain-Scheduling Data-Enabled Predictive Control for Nonlinear Systems with Linearized Operating Regions

Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda +2

This paper presents a Gain-Scheduled Data-Enabled Predictive Control (GS-DeePC) framework for nonlinear systems based on multiple locally linear data representations. Instead of re…

math.OC20241 cited

Distributed Dual Quaternion Extended Kalman Filtering for Spacecraft Pose Estimation

Mathias Hudoba de Badyn, Jonas Binz, Andrea Iannelli +1

In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is a…

math.OC20216 cited

Network Optimization for Edge Consensus

Omar Farhat, Dany Abou Jaoude, Mathias Hudoba de Badyn

This paper examines the performance problem of the edge agreement protocol for networks of agents operating on independent time scales, connected by weighted e…

eess.SY202033 cited

Input Convex Neural Networks for Building MPC

Felix Bünning, Adrian Schalbetter, Ahmed Aboudonia +3

Model Predictive Control in buildings can significantly reduce their energy consumption. The cost and effort necessary for creating and maintaining first principle models for build…

math.OC2020

Graph-theoretic optimization for edge consensus

Mathias Hudoba de Badyn, Dillon R. Foight, Daniel Calderone +2

We consider network structures that optimize the norm of weighted, time scaled consensus networks, under a minimal representation of such consensus networks describ…