2 citations · 4 across the 4 of their papers we have counts for
10 papers
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
Franck Djeumou, Cyrus Neary, Eric Goubault +2
Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown co…
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
Franck Djeumou, Cyrus Neary, Eric Goubault +2
Effective inclusion of physics-based knowledge into deep neural network models of dynamical systems can greatly improve data efficiency and generalization. Such a-priori knowledge…
Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach
Franck Djeumou, Zhe Xu, Murat Cubuktepe +1
We develop a probabilistic control algorithm, , for swarms of agents with heterogeneous dynamics and objectives, subject to high-level task specifications. The r…
Safety-Constrained Learning and Control using Scarce Data and Reciprocal Barriers
Christos K. Verginis, Franck Djeumou, Ufuk Topcu
We develop a control algorithm that ensures the safety, in terms of confinement in a set, of a system with unknown, 2nd-order nonlinear dynamics. The algorithm establishes novel co…
Task-Guided Inverse Reinforcement Learning Under Partial Information
Franck Djeumou, Murat Cubuktepe, Craig Lennon +1
We study the problem of inverse reinforcement learning (IRL), where the learning agent recovers a reward function using expert demonstrations. Most of the existing IRL techniques m…
On-The-Fly Control of Unknown Systems: From Side Information to Performance Guarantees through Reachability
Franck Djeumou, Abraham P. Vinod, Eric Goubault +2
We develop data-driven algorithms for reachability analysis and control of systems with a priori unknown nonlinear dynamics. The resulting algorithms not only are suitable for sett…