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ORCIDJoel A. Paulson
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2020 – today
- 2025
[j16]Yen-An Lu
, Wei-Shou Hu, Joel A. Paulson, Qi Zhang:
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification. Comput. Chem. Eng. 192: 108859 (2025)- [i18]Madhav Muthyala, Farshud Sorourifar, You Peng, Joel A. Paulson:
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond. CoRR abs/2502.03367 (2025) - 2024
- [j15]Akshay Kudva, Wei-Ting Tang, Joel A. Paulson:
Robust Bayesian optimization for flexibility analysis of expensive simulation-based models with rigorous uncertainty bounds. Comput. Chem. Eng. 181: 108515 (2024) - [j14]Madeline E. Scyphers, Justine E. C. Missik, Haley Kujawa, Joel A. Paulson, Gil Bohrer:
Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization. Environ. Model. Softw. 182: 106191 (2024) - [j13]Kimberly J. Chan, Joel A. Paulson, Ali Mesbah:
A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems. IEEE Trans. Control. Syst. Technol. 32(6): 2178-2193 (2024) - [c37]Joel A. Paulson:
Predictive Analytics for Chemical Processes. ACC 2024: 770 - [c36]Farshud Sorourifar, Joel A. Paulson, Ye Wang, Rien Quirynen, Christopher R. Laughman, Ankush Chakrabarty:
Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems. CCTA 2024: 414-419 - [c35]Ankush Chakrabarty, Luigi Vanfretti, Wei-Ting Tang, Joel A. Paulson, Sicheng Zhan, Scott A. Bortoff, Vedang M. Deshpande, Ye Wang, Christopher R. Laughman:
Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks. CCTA 2024: 547-554 - [c34]Wei-Ting Tang, Joel A. Paulson:
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization. CDC 2024: 1547-1552 - [i17]Farshud Sorourifar, Thomas Banker, Joel A. Paulson:
Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces. CoRR abs/2401.01398 (2024) - [i16]Joel A. Paulson, Calvin Tsay:
Bayesian optimization as a flexible and efficient design framework for sustainable process systems. CoRR abs/2401.16373 (2024) - [i15]Wei-Ting Tang, Joel A. Paulson:
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization. CoRR abs/2405.07760 (2024) - [i14]Yen-An Lu, Wei-Shou Hu, Joel A. Paulson, Qi Zhang:
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification. CoRR abs/2405.17875 (2024) - [i13]Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson:
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems. CoRR abs/2406.03616 (2024) - [i12]Prabhat K. Mishra, Joel A. Paulson, Richard D. Braatz:
Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions. CoRR abs/2406.10734 (2024) - [i11]Madhav Muthyala, Farshud Sorourifar, Joel A. Paulson:
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery. CoRR abs/2410.01752 (2024) - [i10]Yilin Xie, Shiqiang Zhang, Joel A. Paulson, Calvin Tsay:
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation. CoRR abs/2410.16893 (2024) - 2023
- [j12]Tong Zhao, Ekim Yurtsever, Joel A. Paulson, Giorgio Rizzoni:
Formal Certification Methods for Automated Vehicle Safety Assessment. IEEE Trans. Intell. Veh. 8(1): 232-249 (2023) - [c33]Joel A. Paulson, Farshud Sorourifar, Christopher R. Laughman, Ankush Chakrabarty:
LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems. ACC 2023: 576-582 - [c32]Joel A. Paulson, Farshud Sorourifar, Ali Mesbah:
A Tutorial on Derivative-Free Policy Learning Methods for Interpretable Controller Representations. ACC 2023: 1295-1306 - [c31]Truong X. Nghiem, Ján Drgona, Colin N. Jones, Zoltán Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw-Cortez, Draguna L. Vrabie:
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. ACC 2023: 3735-3750 - [c30]Georgios Makrygiorgos, Joel A. Paulson, Ali Mesbah:
No-Regret Bayesian Optimization with Gradients Using Local Optimality-Based Constraints: Application to Closed-Loop Policy Search. CDC 2023: 20-25 - [c29]Kimberly J. Chan, Joel A. Paulson, Ali Mesbah:
Safe Explorative Bayesian Optimization - Towards Personalized Treatments in Plasma Medicine. CDC 2023: 4106-4111 - [i9]Dinesh Krishnamoorthy, Joel A. Paulson:
Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers. CoRR abs/2303.14414 (2023) - [i8]Congwen Lu, Joel A. Paulson:
No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations. CoRR abs/2305.03824 (2023) - [i7]Truong X. Nghiem, Ján Drgona, Colin N. Jones, Zoltán Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw-Cortez, Draguna L. Vrabie:
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. CoRR abs/2306.13867 (2023) - 2022
- [j11]Joel A. Paulson, Congwen Lu:
COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information. Comput. Chem. Eng. 160: 107700 (2022) - [j10]Jared O'Leary, Joel A. Paulson, Ali Mesbah:
Stochastic physics-informed neural ordinary differential equations. J. Comput. Phys. 468: 111466 (2022) - [c28]Ali Mesbah, Kim Peter Wabersich, Angela P. Schoellig, Melanie N. Zeilinger, Sergio Lucia, Thomas A. Badgwell, Joel A. Paulson:
Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon? ACC 2022: 342-357 - [c27]Akshay Kudva, Farshud Sorourifar, Joel A. Paulson:
Efficient Robust Global Optimization for Simulation-based Problems using Decomposed Gaussian Processes: Application to MPC Calibration. ACC 2022: 2091-2097 - [c26]Bhavik R. Bakshi, Joel A. Paulson:
Sustainability and Industry 4.0: Obstacles and Opportunities*. ACC 2022: 2449-2460 - [c25]Joel A. Paulson, Farshud Sorourifar, Ankush Chakrabarty:
Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints. CDC 2022: 123-129 - [c24]Angelo D. Bonzanini, Joel A. Paulson, Georgios Makrygiorgos, Ali Mesbah:
Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning. CDC 2022: 3431-3437 - [i6]Tong Zhao, Ekim Yurtsever, Joel A. Paulson, Giorgio Rizzoni:
Automated Vehicle Safety Guarantee, Verification and Certification: A Survey. CoRR abs/2202.02818 (2022) - 2021
- [j9]Angelo D. Bonzanini, Joel A. Paulson, Georgios Makrygiorgos, Ali Mesbah:
Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks. Comput. Chem. Eng. 145: 107174 (2021) - [j8]Joel A. Paulson, Ali Mesbah:
Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees. IEEE Control. Syst. Lett. 5(4): 1477-1482 (2021) - [c23]Joel A. Paulson, Ali Mesbah:
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees. ACC 2021: 2102-2107 - [c22]Naitik A. Choksi, Joel A. Paulson:
Simulation-based Integrated Design and Control with Embedded Mixed-Integer MPC using Constrained Bayesian Optimization. ACC 2021: 2114-2120 - [c21]Kimberly J. Chan, Joel A. Paulson, Ali Mesbah:
Deep Learning-based Approximate Nonlinear Model Predictive Control with Offset-free Tracking for Embedded Applications. ACC 2021: 3475-3481 - [c20]Joel A. Paulson, Ketong Shao, Ali Mesbah:
Probabilistically Robust Bayesian Optimization for Data-Driven Design of Arbitrary Controllers with Gaussian Process Emulators. CDC 2021: 3633-3639 - [i5]Jared O'Leary, Joel A. Paulson, Ali Mesbah:
Stochastic Physics-Informed Neural Networks (SPINN): A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equations. CoRR abs/2109.01621 (2021) - 2020
- [j7]Ali Mesbah, Joel A. Paulson, Richard D. Braatz:
An internal model control design method for failure-tolerant control with multiple objectives. Comput. Chem. Eng. 140: 106955 (2020) - [j6]Joel A. Paulson, Ali Mesbah:
Approximate Closed-Loop Robust Model Predictive Control With Guaranteed Stability and Constraint Satisfaction. IEEE Control. Syst. Lett. 4(3): 719-724 (2020) - [j5]Joel A. Paulson, Edward A. Buehler, Richard D. Braatz, Ali Mesbah:
Stochastic model predictive control with joint chance constraints. Int. J. Control 93(1): 126-139 (2020) - [c19]Joel A. Paulson, Ali Mesbah:
A Low-complexity Tube Controller using Contractive Invariant Sets. CDC 2020: 899-904 - [c18]Angelo D. Bonzanini, Joel A. Paulson, Ali Mesbah:
Safe Learning-based Model Predictive Control under State- and Input-dependent Uncertainty using Scenario Trees. CDC 2020: 2448-2454 - [i4]Joel A. Paulson, Ali Mesbah:
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees. CoRR abs/2011.07445 (2020) - [i3]Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah, Joel A. Paulson:
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization. CoRR abs/2011.11841 (2020)
2010 – 2019
- 2019
- [j4]Joel A. Paulson, Marc Martin-Casas, Ali Mesbah:
Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions. PLoS Comput. Biol. 15(8) (2019) - [c17]Joel A. Paulson, Tor Aksel N. Heirung, Ali Mesbah:
Fault-Tolerant Tube-Based Robust Nonlinear Model Predictive Control. ACC 2019: 1648-1654 - 2018
- [j3]Tor Aksel N. Heirung, Joel A. Paulson, Jared O'Leary, Ali Mesbah:
Stochastic model predictive control - how does it work? Comput. Chem. Eng. 114: 158-170 (2018) - [c16]Joel A. Paulson, Tor Aksel N. Heirung, Richard D. Braatz, Ali Mesbah:
Closed-Loop Active Fault Diagnosis for Stochastic Linear Systems. ACC 2018: 735-741 - [c15]Tito L. M. Santos, Joel A. Paulson, Ali Mesbah:
Stochastic Model Predictive Control with Enlarged Domain of Attraction for Offset-Free Tracking. ACC 2018: 742-748 - [c14]Joel A. Paulson, Ali Mesbah:
Shaping the Closed-Loop Behavior of Nonlinear Systems Under Probabilistic Uncertainty Using Arbitrary Polynomial Chaos. CDC 2018: 6307-6313 - 2017
- [c13]Sergio Lucia, Joel A. Paulson, Rolf Findeisen, Richard D. Braatz:
On stability of stochastic linear systems via polynomial chaos expansions. ACC 2017: 5089-5094 - 2016
- [j2]Mengling Wang, Joel A. Paulson, Huaicheng Yan, Hongbo Shi:
An Adaptive Model Predictive Control Strategy for Nonlinear Distributed Parameter Systems using the Type-2 Takagi-Sugeno Model. Int. J. Fuzzy Syst. 18(5): 792-805 (2016) - [c12]Eranda Harinath, Lucas C. Foguth, Joel A. Paulson, Richard D. Braatz:
Nonlinear model predictive control using polynomial optimization methods. ACC 2016: 1-6 - [c11]Amos E. Lu, Joel A. Paulson, Richard D. Braatz:
pH and conductivity control in an integrated biomanufacturing plant. ACC 2016: 1741-1746 - [c10]Tillmann Muhlpfordt, Joel A. Paulson, Richard D. Braatz, Rolf Findeisen:
Output feedback model predictive control with probabilistic uncertainties for linear systems. ACC 2016: 2035-2040 - [c9]Joel A. Paulson, Venkatasailanathan Ramadesigan, Venkat R. Subramanian, Richard D. Braatz:
Control systems analysis and design of multiscale simulation models. ACC 2016: 3083-3085 - [c8]Edward A. Buehler, Joel A. Paulson, Ali Mesbah:
Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions. ACC 2016: 5389-5394 - 2015
- [j1]Benben Jiang, Xiaoxiang Zhu, Dexian Huang, Joel A. Paulson, Richard D. Braatz:
A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis. Comput. Chem. Eng. 77: 1-9 (2015) - [c7]Amos E. Lu, Joel A. Paulson, Nicholas J. Mozdzierz, Alan Stockdale, Ashlee N. Ford Versypt, Kerry R. Love, J. Christopher Love, Richard D. Braatz:
Control systems technology in the advanced manufacturing of biologic drugs. CCA 2015: 1505-1515 - [c6]Joel A. Paulson, Stefan Streif, Ali Mesbah:
Stability for receding-horizon stochastic model predictive control. ACC 2015: 937-943 - [c5]Ali Mesbah, Joel A. Paulson, Richard Lakerveld, Richard D. Braatz:
Plant-wide model predictive control for a continuous pharmaceutical process. ACC 2015: 4301-4307 - [c4]Marcello Torchio, Nicolas A. Wolff, Davide Martino Raimondo, Lalo Magni, Ulrike Krewer, R. Bhushan Gopaluni, Joel A. Paulson, Richard D. Braatz:
Real-time model predictive control for the optimal charging of a lithium-ion battery. ACC 2015: 4536-4541 - [c3]Lucas C. Foguth, Joel A. Paulson, Richard D. Braatz, Davide Martino Raimondo:
Fast robust model predictive control of high-dimensional systems. ECC 2015: 2009-2014 - [i2]Edward A. Buehler, Joel A. Paulson, Ali Akhavan, Ali Mesbah:
Lyapunov-based Stochastic Nonlinear Model Predictive Control: Shaping the State Probability Density Functions. CoRR abs/1505.02871 (2015) - [i1]Joel A. Paulson, Edward A. Buehler, Richard D. Braatz, Ali Mesbah:
Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints. CoRR abs/1506.08471 (2015) - 2014
- [c2]Joel A. Paulson, Ali Mesbah, Stefan Streif, Rolf Findeisen, Richard D. Braatz:
Fast stochastic model predictive control of high-dimensional systems. CDC 2014: 2802-2809 - [c1]Joel A. Paulson, Davide Martino Raimondo, Rolf Findeisen, Richard D. Braatz, Stefan Streif:
Guaranteed active fault diagnosis for uncertain nonlinear systems. ECC 2014: 926-931
Coauthor Index
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