132 citations · 317 across the 27 of their papers we have counts for
28 papers
Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou +17
The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and sol…
Rethinking Trust Region Bayesian Optimization in High Dimensions
Wei-Ting Tang, Joel A. Paulson
Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimization. However, inappropriate l…
An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
Wei-Ting Tang, Akshay Kudva, Calvin Tsay +1
We study the deterministic global optimization of trained Gaussian process posterior mean functions over hyperrectangular domains. Although the posterior mean function has a compac…
BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertainty
Akshay Kudva, Joel A. Paulson
Optimal design under uncertainty remains a fundamental challenge in advancing reliable, next-generation process systems. Robust optimization (RO) offers a principled approach by sa…
NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
Wei-Ting Tang, Akshay Kudva, Joel A. Paulson
Bayesian optimization (BO) is effective for expensive black-box problems but remains challenging in high dimensions. We propose NeST-BO, a curvature-aware local BO method that targ…
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
Madhav R. Muthyala, Farshud Sorourifar, You Peng +1
Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data. Although a wide-variety of SR…
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
Designing modern industrial systems requires balancing several competing objectives, such as profitability, resilience, and sustainability, while accounting for complex interaction…
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Yilin Xie, Shiqiang Zhang, Joel A. Paulson +1
Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non-convex optimization problem itself. D…
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
Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact represe…
Towards Efficient Quantum Computation of Molecular Ground State Energies using Bayesian Optimization with Priors over Surface Topology
Farshud Sorourifar, Mohamed Taha Rouabah, Nacer Eddine Belaloui +6
Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classic…
Bayesian Optimization Priors for Efficient Variational Quantum Algorithms
Farshud Sorourifar, Diana Chamaki, Norm M. Tubman +2
Quantum computers currently rely on a hybrid quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. Still, there are several challenges with V…
Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions
Prabhat K. Mishra, Joel A. Paulson, Richard D. Braatz
This article is devoted to providing a review of mathematical formulations in which Polynomial Chaos Theory (PCT) has been incorporated into stochastic model predictive control (SM…
BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search
Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson
Novelty search (NS) aims to uncover diverse system behaviors through simulation or experiment without requiring a pre-specified scalar objective. This capability is especially rele…
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
Yen-An Lu, Wei-Shou Hu, Joel A. Paulson +1
This work addresses data-driven inverse optimization (IO), where the goal is to estimate unknown parameters in an optimization model from observed decisions that can be assumed to…
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization
Wei-Ting Tang, Joel A. Paulson
Bayesian optimization (BO) is a popular approach for optimizing expensive-to-evaluate black-box objective functions. An important challenge in BO is its application to high-dimensi…
Bayesian optimization as a flexible and efficient design framework for sustainable process systems
Joel A. Paulson, Calvin Tsay
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engine…
Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces
Farshud Sorourifar, Thomas Banker, Joel A. Paulson
Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive s…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations
Congwen Lu, Joel A. Paulson
This paper investigates the problem of efficient constrained global optimization of hybrid models that are a composition of a known white-box function and an expensive multi-output…
Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers
Dinesh Krishnamoorthy, Joel A. Paulson
Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off betw…
Formal Certification Methods for Automated Vehicle Safety Assessment
Tong Zhao, Ekim Yurtsever, Joel Paulson +1
Challenges related to automated driving are no longer focused on just the construction of such automated vehicles (AVs), but in assuring the safety of their operation. Recent advan…
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…
COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information
Joel A. Paulson, Congwen Lu
Many engineering problems involve the optimization of computationally expensive models for which derivative information is not readily available. The Bayesian optimization (BO) fra…
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…