Fast stochastic model predictive control of high-dimensional systems. - Joel A. Paulson; Ali Mesbah 002; Stefan Streif; Rolf Findeisen; Richard D. Braatz (CDC, 2014)

Guaranteed active fault diagnosis for uncertain nonlinear systems. - Joel A. Paulson; Davide Martino Raimondo; Rolf Findeisen; Richard D. Braatz; Stefan Streif (ECC, 2014)

Control systems technology in the advanced manufacturing of biologic drugs. - Amos E. Lu; Joel A. Paulson; Nicholas J. Mozdzierz; Alan Stockdale; Ashlee N. Ford Versypt; Kerry R. Love; J. Christopher Love; Richard D. Braatz (CCA, 2015)

Plant-wide model predictive control for a continuous pharmaceutical process. - Ali Mesbah 002; Joel A. Paulson; Richard Lakerveld; Richard D. Braatz (ACC, 2015)

Stability for receding-horizon stochastic model predictive control. - Joel A. Paulson; Stefan Streif; Ali Mesbah 002 (ACC, 2015)

Real-time model predictive control for the optimal charging of a lithium-ion battery. - Marcello Torchio; Nicolas A. Wolff; Davide Martino Raimondo; Lalo Magni; Ulrike Krewer; R. Bhushan Gopaluni; Joel A. Paulson; Richard D. Braatz (ACC, 2015)

Fast robust model predictive control of high-dimensional systems. - Lucas C. Foguth; Joel A. Paulson; Richard D. Braatz; Davide Martino Raimondo (ECC, 2015)

A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis. - Benben Jiang; Xiaoxiang Zhu 005; Dexian Huang; Joel A. Paulson; Richard D. Braatz (Comput. Chem. Eng., 2015)

Lyapunov-based Stochastic Nonlinear Model Predictive Control: Shaping the State Probability Density Functions. - Edward A. Buehler; Joel A. Paulson; Ali Akhavan; Ali Mesbah 002 (CoRR, 2015)

Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints. - Joel A. Paulson; Edward A. Buehler; Richard D. Braatz; Ali Mesbah 002 (CoRR, 2015)

Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions. - Edward A. Buehler; Joel A. Paulson; Ali Mesbah 002 (ACC, 2016)

Nonlinear model predictive control using polynomial optimization methods. - Eranda Harinath; Lucas C. Foguth; Joel A. Paulson; Richard D. Braatz (ACC, 2016)

pH and conductivity control in an integrated biomanufacturing plant. - Amos E. Lu; Joel A. Paulson; Richard D. Braatz (ACC, 2016)

Output feedback model predictive control with probabilistic uncertainties for linear systems. - Tillmann Muhlpfordt; Joel A. Paulson; Richard D. Braatz; Rolf Findeisen (ACC, 2016)

Control systems analysis and design of multiscale simulation models. - Joel A. Paulson; Venkatasailanathan Ramadesigan; Venkat R. Subramanian 001; Richard D. Braatz (ACC, 2016)

An Adaptive Model Predictive Control Strategy for Nonlinear Distributed Parameter Systems using the Type-2 Takagi-Sugeno Model. - Mengling Wang; Joel A. Paulson; Huaicheng Yan 001; Hongbo Shi (Int. J. Fuzzy Syst., 2016)

On stability of stochastic linear systems via polynomial chaos expansions. - Sergio Lucia; Joel A. Paulson; Rolf Findeisen; Richard D. Braatz (ACC, 2017)

Closed-Loop Active Fault Diagnosis for Stochastic Linear Systems. - Joel A. Paulson; Tor Aksel N. Heirung; Richard D. Braatz; Ali Mesbah 002 (ACC, 2018)

Stochastic Model Predictive Control with Enlarged Domain of Attraction for Offset-Free Tracking. - Tito L. M. Santos; Joel A. Paulson; Ali Mesbah 002 (ACC, 2018)

Shaping the Closed-Loop Behavior of Nonlinear Systems Under Probabilistic Uncertainty Using Arbitrary Polynomial Chaos. - Joel A. Paulson; Ali Mesbah 002 (CDC, 2018)

Stochastic model predictive control - how does it work? - Tor Aksel N. Heirung; Joel A. Paulson; Jared O'Leary; Ali Mesbah 002 (Comput. Chem. Eng., 2018)

Fault-Tolerant Tube-Based Robust Nonlinear Model Predictive Control. - Joel A. Paulson; Tor Aksel N. Heirung; Ali Mesbah 002 (ACC, 2019)

Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions. - Joel A. Paulson; Marc Martin-Casas; Ali Mesbah 002 (PLoS Comput. Biol., 2019)

Safe Learning-based Model Predictive Control under State- and Input-dependent Uncertainty using Scenario Trees. - Angelo D. Bonzanini; Joel A. Paulson; Ali Mesbah 002 (CDC, 2020)

A Low-complexity Tube Controller using Contractive Invariant Sets. - Joel A. Paulson; Ali Mesbah 002 (CDC, 2020)

An internal model control design method for failure-tolerant control with multiple objectives. - Ali Mesbah 002; Joel A. Paulson; Richard D. Braatz (Comput. Chem. Eng., 2020)

A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization. - Farshud Sorourifar; Georgios Makrygirgos; Ali Mesbah 002; Joel A. Paulson (CoRR, 2020)

Approximate Closed-Loop Robust Model Predictive Control With Guaranteed Stability and Constraint Satisfaction. - Joel A. Paulson; Ali Mesbah 002 (IEEE Control. Syst. Lett., 2020)

Stochastic model predictive control with joint chance constraints. - Joel A. Paulson; Edward A. Buehler; Richard D. Braatz; Ali Mesbah 002 (Int. J. Control, 2020)

Deep Learning-based Approximate Nonlinear Model Predictive Control with Offset-free Tracking for Embedded Applications. - Kimberly J. Chan; Joel A. Paulson; Ali Mesbah 002 (ACC, 2021)

Simulation-based Integrated Design and Control with Embedded Mixed-Integer MPC using Constrained Bayesian Optimization. - Naitik A. Choksi; Joel A. Paulson (ACC, 2021)

Probabilistically Robust Bayesian Optimization for Data-Driven Design of Arbitrary Controllers with Gaussian Process Emulators. - Joel A. Paulson; Ketong Shao; Ali Mesbah 002 (CDC, 2021)

Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks. - Angelo D. Bonzanini; Joel A. Paulson; Georgios Makrygiorgos; Ali Mesbah 002 (Comput. Chem. Eng., 2021)

Stochastic Physics-Informed Neural Networks (SPINN): A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equations. - Jared O'Leary; Joel A. Paulson; Ali Mesbah 002 (CoRR, 2021)

Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees. - Joel A. Paulson; Ali Mesbah 002 (IEEE Control. Syst. Lett., 2021)

Sustainability and Industry 4.0: Obstacles and Opportunities*. - Bhavik R. Bakshi; Joel A. Paulson (ACC, 2022)

Efficient Robust Global Optimization for Simulation-based Problems using Decomposed Gaussian Processes: Application to MPC Calibration. - Akshay Kudva; Farshud Sorourifar; Joel A. Paulson (ACC, 2022)

Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon? - Ali Mesbah 002; Kim Peter Wabersich; Angela P. Schoellig; Melanie N. Zeilinger; Sergio Lucia; Thomas A. Badgwell; Joel A. Paulson (ACC, 2022)

Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning. - Angelo D. Bonzanini; Joel A. Paulson; Georgios Makrygiorgos; Ali Mesbah 002 (CDC, 2022)

Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints. - Joel A. Paulson; Farshud Sorourifar; Ankush Chakrabarty (CDC, 2022)

COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information. - Joel A. Paulson; Congwen Lu (Comput. Chem. Eng., 2022)

Automated Vehicle Safety Guarantee, Verification and Certification: A Survey. - Tong Zhao; Ekim Yurtsever; Joel A. Paulson; Giorgio Rizzoni (CoRR, 2022)

Stochastic physics-informed neural ordinary differential equations. - Jared O'Leary; Joel A. Paulson; Ali Mesbah 002 (J. Comput. Phys., 2022)

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. - Truong X. Nghiem; Ján Drgona; Colin N. Jones; Zoltán Nagy 002; Roland Schwan; Biswadip Dey; Ankush Chakrabarty; Stefano Di Cairano; Joel A. Paulson; Andrea Carron; Melanie N. Zeilinger; Wenceslao Shaw-Cortez; et al. (ACC, 2023)

A Tutorial on Derivative-Free Policy Learning Methods for Interpretable Controller Representations. - Joel A. Paulson; Farshud Sorourifar; Ali Mesbah 002 (ACC, 2023)

LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems. - Joel A. Paulson; Farshud Sorourifar; Christopher R. Laughman; Ankush Chakrabarty (ACC, 2023)

Safe Explorative Bayesian Optimization - Towards Personalized Treatments in Plasma Medicine. - Kimberly J. Chan; Joel A. Paulson; Ali Mesbah 002 (CDC, 2023)

No-Regret Bayesian Optimization with Gradients Using Local Optimality-Based Constraints: Application to Closed-Loop Policy Search. - Georgios Makrygiorgos; Joel A. Paulson; Ali Mesbah 002 (CDC, 2023)

Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers. - Dinesh Krishnamoorthy 002; Joel A. Paulson (CoRR, 2023)

No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations. - Congwen Lu; Joel A. Paulson (CoRR, 2023)

Formal Certification Methods for Automated Vehicle Safety Assessment. - Tong Zhao; Ekim Yurtsever; Joel A. Paulson; Giorgio Rizzoni (IEEE Trans. Intell. Veh., 2023)

Predictive Analytics for Chemical Processes. - Joel A. Paulson (ACC, 2024)

Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks. - Ankush Chakrabarty; Luigi Vanfretti; Wei-Ting Tang; Joel A. Paulson; Sicheng Zhan; Scott A. Bortoff; Vedang M. Deshpande; Ye Wang 001; Christopher R. Laughman (CCTA, 2024)

Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems. - Farshud Sorourifar; Joel A. Paulson; Ye Wang 001; Rien Quirynen; Christopher R. Laughman; Ankush Chakrabarty (CCTA, 2024)

CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization. - Wei-Ting Tang; Joel A. Paulson (CDC, 2024)

Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces. - Farshud Sorourifar; Thomas Banker; Joel A. Paulson (CoRR, 2024)

Bayesian optimization as a flexible and efficient design framework for sustainable process systems. - Joel A. Paulson; Calvin Tsay (CoRR, 2024)

BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems. - Wei-Ting Tang; Ankush Chakrabarty; Joel A. Paulson (CoRR, 2024)

Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions. - Prabhat K. Mishra; Joel A. Paulson; Richard D. Braatz (CoRR, 2024)

TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery. - Madhav Muthyala; Farshud Sorourifar; Joel A. Paulson (CoRR, 2024)

Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization. - Madeline E. Scyphers; Justine E. C. Missik; Haley Kujawa; Joel A. Paulson; Gil Bohrer (Environ. Model. Softw., 2024)

Robust Bayesian optimization for flexibility analysis of expensive simulation-based models with rigorous uncertainty bounds. - Akshay Kudva; Wei-Ting Tang; Joel A. Paulson (Comput. Chem. Eng., 2024)

A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems. - Kimberly J. Chan; Joel A. Paulson; Ali Mesbah 002 (IEEE Trans. Control. Syst. Technol., 2024)

Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation. - Yilin Xie; Shiqiang Zhang; Joel A. Paulson; Calvin Tsay (AISTATS, 2025)

Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering. - Qi Zhang 016; Styliani Avraamidou; Joel A. Paulson; Vyom Thakker; Zhenyu Wang; Leo Chiang; Birgit Braun; Tushar Rathi; Ankush Chakrabarty; Farshud Sorourifar; Wei-Ting Tang; Paola Munoz; et al. (ACC, 2025)

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification. - Yen-An Lu; Wei-Shou Hu; Joel A. Paulson; Qi Zhang 016 (Comput. Chem. Eng., 2025)

SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond. - Madhav Muthyala; Farshud Sorourifar; You Peng; Joel A. Paulson (CoRR, 2025)

Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs. - Akshay Kudva; Wei-Ting Tang; Joel A. Paulson (CoRR, 2025)