Joel A. Paulson

Orcid: 0000-0002-1518-7985

According to our database1, Joel A. Paulson authored at least 54 papers between 2014 and 2024.

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Bibliography

2024
Robust Bayesian optimization for flexibility analysis of expensive simulation-based models with rigorous uncertainty bounds.
Comput. Chem. Eng., February, 2024

Bayesian optimization as a flexible and efficient design framework for sustainable process systems.
CoRR, 2024

Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces.
CoRR, 2024

2023
Formal Certification Methods for Automated Vehicle Safety Assessment.
IEEE Trans. Intell. Veh., January, 2023

No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations.
CoRR, 2023

Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers.
CoRR, 2023

No-Regret Bayesian Optimization with Gradients Using Local Optimality-Based Constraints: Application to Closed-Loop Policy Search.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Safe Explorative Bayesian Optimization - Towards Personalized Treatments in Plasma Medicine.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems.
Proceedings of the American Control Conference, 2023

A Tutorial on Derivative-Free Policy Learning Methods for Interpretable Controller Representations.
Proceedings of the American Control Conference, 2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems.
Proceedings of the American Control Conference, 2023

2022
Stochastic physics-informed neural ordinary differential equations.
J. Comput. Phys., 2022

Automated Vehicle Safety Guarantee, Verification and Certification: A Survey.
CoRR, 2022

COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information.
Comput. Chem. Eng., 2022

Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon?
Proceedings of the American Control Conference, 2022

Efficient Robust Global Optimization for Simulation-based Problems using Decomposed Gaussian Processes: Application to MPC Calibration.
Proceedings of the American Control Conference, 2022

Sustainability and Industry 4.0: Obstacles and Opportunities<sup>*</sup>.
Proceedings of the American Control Conference, 2022

2021
Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees.
IEEE Control. Syst. Lett., 2021

Stochastic Physics-Informed Neural Networks (SPINN): A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equations.
CoRR, 2021

Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks.
Comput. Chem. Eng., 2021

Probabilistically Robust Bayesian Optimization for Data-Driven Design of Arbitrary Controllers with Gaussian Process Emulators.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Simulation-based Integrated Design and Control with Embedded Mixed-Integer MPC using Constrained Bayesian Optimization.
Proceedings of the 2021 American Control Conference, 2021

Deep Learning-based Approximate Nonlinear Model Predictive Control with Offset-free Tracking for Embedded Applications.
Proceedings of the 2021 American Control Conference, 2021

2020
Stochastic model predictive control with joint chance constraints.
Int. J. Control, 2020

Approximate Closed-Loop Robust Model Predictive Control With Guaranteed Stability and Constraint Satisfaction.
IEEE Control. Syst. Lett., 2020

A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization.
CoRR, 2020

An internal model control design method for failure-tolerant control with multiple objectives.
Comput. Chem. Eng., 2020

A Low-complexity Tube Controller using Contractive Invariant Sets.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Safe Learning-based Model Predictive Control under State- and Input-dependent Uncertainty using Scenario Trees.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019
Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions.
PLoS Comput. Biol., 2019

Fault-Tolerant Tube-Based Robust Nonlinear Model Predictive Control.
Proceedings of the 2019 American Control Conference, 2019

2018
Stochastic model predictive control - how does it work?
Comput. Chem. Eng., 2018

Shaping the Closed-Loop Behavior of Nonlinear Systems Under Probabilistic Uncertainty Using Arbitrary Polynomial Chaos.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Stochastic Model Predictive Control with Enlarged Domain of Attraction for Offset-Free Tracking.
Proceedings of the 2018 Annual American Control Conference, 2018

Closed-Loop Active Fault Diagnosis for Stochastic Linear Systems.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
On stability of stochastic linear systems via polynomial chaos expansions.
Proceedings of the 2017 American Control Conference, 2017

2016
An Adaptive Model Predictive Control Strategy for Nonlinear Distributed Parameter Systems using the Type-2 Takagi-Sugeno Model.
Int. J. Fuzzy Syst., 2016

Control systems analysis and design of multiscale simulation models.
Proceedings of the 2016 American Control Conference, 2016

Output feedback model predictive control with probabilistic uncertainties for linear systems.
Proceedings of the 2016 American Control Conference, 2016

pH and conductivity control in an integrated biomanufacturing plant.
Proceedings of the 2016 American Control Conference, 2016

Nonlinear model predictive control using polynomial optimization methods.
Proceedings of the 2016 American Control Conference, 2016

Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions.
Proceedings of the 2016 American Control Conference, 2016

2015
Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints.
CoRR, 2015

Lyapunov-based Stochastic Nonlinear Model Predictive Control: Shaping the State Probability Density Functions.
CoRR, 2015

A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis.
Comput. Chem. Eng., 2015

Fast robust model predictive control of high-dimensional systems.
Proceedings of the 14th European Control Conference, 2015

Real-time model predictive control for the optimal charging of a lithium-ion battery.
Proceedings of the American Control Conference, 2015

Stability for receding-horizon stochastic model predictive control.
Proceedings of the American Control Conference, 2015

Plant-wide model predictive control for a continuous pharmaceutical process.
Proceedings of the American Control Conference, 2015

Control systems technology in the advanced manufacturing of biologic drugs.
Proceedings of the 2015 IEEE Conference on Control Applications, 2015

2014
Guaranteed active fault diagnosis for uncertain nonlinear systems.
Proceedings of the 13th European Control Conference, 2014

Fast stochastic model predictive control of high-dimensional systems.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014


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