132 citations · 330 across the 12 of their papers we have counts for
14 papers
Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly
Iman Nodozi, Charlie Yan, Mira Khare +2
We show that the minimum effort control of colloidal self-assembly can be naturally formulated in the order-parameter space as a generalized Schrödinger bridge problem -- a class o…
A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly
Iman Nodozi, Jared O'Leary, Ali Mesbah +1
We propose formulating the finite-horizon stochastic optimal control problem for colloidal self-assembly in the space of probability density functions (PDFs) of the underlying stat…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
Safe Exploration and Escape Local Minima with Model Predictive Control under Partially Unknown Constraints
Raffaele Soloperto, Ali Mesbah, Frank Allgöwer
In this paper, we propose a novel model predictive control (MPC) framework for output tracking that deals with partially unknown constraints. The MPC scheme optimizes over a learni…
Stochastic Physics-Informed Neural Ordinary Differential Equations
Jared O'Leary, Joel A. Paulson, Ali Mesbah
Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems. Learning the hidden physics within SDEs is crucial for unravel…
Low Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap
Mounir Laroussi, Sander Bekeschus, Michael Keidar +18
Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas a…
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization
Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah +1
The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, predicti…
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees
Joel A. Paulson, Ali Mesbah
Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of blackbox optimizatio…
PoCET: a Polynomial Chaos Expansion Toolbox for Matlab
Felix Petzke, Ali Mesbah, Stefan Streif
We introduce PoCET: a free and open-scource Polynomial Chaos Expansion Toolbox for Matlab, featuring the automatic generation of polynomial chaos expansion (PCE) for linear and non…
Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints
Joel A. Paulson, Edward A. Buehler, Richard D. Braatz +1
This article considers the stochastic optimal control of discrete-time linear systems subject to (possibly) unbounded stochastic disturbances, hard constraints on the manipulated v…
Lyapunov-based Stochastic Nonlinear Model Predictive Control: Shaping the State Probability Density Functions
Edward A. Buehler, Joel A. Paulson, Ali Akhavan +1
Stochastic uncertainties in complex dynamical systems lead to variability of system states, which can in turn degrade the closed-loop performance. This paper presents a stochastic…
A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints
Ali Mesbah, Stefan Streif
Accurate estimation of parameters is paramount in developing high-fidelity models for complex dynamical systems. Model-based optimal experiment design (OED) approaches enable syste…
Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints
Stefan Streif, Matthias Karl, Ali Mesbah
This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial condi…
Stability for Receding-horizon Stochastic Model Predictive Control
Joel A. Paulson, Stefan Streif, Ali Mesbah
A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian…