18 citations · 18 across the 12 of their papers we have counts for
16 papers
Learning Nonlinear Continuous-Time Systems for Formal Uncertainty Propagation and Probabilistic Evaluation
Peter Amorese, Morteza Lahijanian
Nonlinear ordinary differential equations (ODEs) are powerful tools for modeling real-world dynamical systems. However, propagating initial state uncertainty through nonlinear dyna…
Data-Driven Control via Conditional Mean Embeddings: Formal Guarantees via Uncertain MDP Abstraction
Ibon Gracia, Morteza Lahijanian
Controlling stochastic systems with unknown dynamics and under complex specifications is specially challenging in safety-critical settings, where performance guarantees are essenti…
Universal Learning of Stochastic Dynamics for Exact Belief Propagation using Bernstein Normalizing Flows
Peter Amorese, Morteza Lahijanian
Predicting the distribution of future states in a stochastic system, known as belief propagation, is fundamental to reasoning under uncertainty. However, nonlinear dynamics often m…
Bayesian Diagnosability and Active Fault Identification
Chun-Wei Kong, Jay McMahon, Morteza Lahijanian
We study fault identification in discrete-time nonlinear systems subject to additive Gaussian white noise. We introduce a Bayesian framework that explicitly accounts for unmodeled…
Piecewise Control Barrier Functions for Stochastic Systems
Rayan Mazouz, Luca Laurenti, Morteza Lahijanian
This paper presents a method for the simultaneous synthesis of a barrier certificate and a safe controller for discrete-time nonlinear stochastic systems. Our approach, based on pi…
Beyond Interval MDPs: Tight and Efficient Abstractions of Stochastic Systems
Ibon Gracia, Morteza Lahijanian
This work addresses the general problem of control synthesis for continuous-space, discrete-time stochastic systems with probabilistic guarantees via finite abstractions. While est…