Joel A. Paulson
Orcid: 0000-0002-1518-7985
According to our database1,
Joel A. Paulson
authored at least 66 papers
between 2014 and 2025.
Collaborative distances:
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Bibliography
2025
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond.
CoRR, February, 2025
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification.
Comput. Chem. Eng., 2025
2024
A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems.
IEEE Trans. Control. Syst. Technol., November, 2024
Robust Bayesian optimization for flexibility analysis of expensive simulation-based models with rigorous uncertainty bounds.
Comput. Chem. Eng., February, 2024
Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization.
Environ. Model. Softw., 2024
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation.
CoRR, 2024
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery.
CoRR, 2024
Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions.
CoRR, 2024
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems.
CoRR, 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
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization.
Proceedings of the 63rd IEEE Conference on Decision and Control, 2024
Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems.
Proceedings of the IEEE Conference on Control Technology and Applications, 2024
Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks.
Proceedings of the IEEE Conference on Control Technology and Applications, 2024
Proceedings of the American Control Conference, 2024
2023
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
Proceedings of the American Control Conference, 2023
2022
J. Comput. Phys., 2022
CoRR, 2022
COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information.
Comput. Chem. Eng., 2022
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
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
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
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
Proceedings of the 2019 American Control Conference, 2019
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
Proceedings of the 2018 Annual American Control Conference, 2018
2017
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
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
Proceedings of the 2016 American Control Conference, 2016
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
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
Proceedings of the American Control Conference, 2015
Proceedings of the American Control Conference, 2015
Proceedings of the 2015 IEEE Conference on Control Applications, 2015
2014
Proceedings of the 13th European Control Conference, 2014
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014