340 citations · 1.6k across the 14 of their papers we have counts for
14 papers
Online Learning Based Risk-Averse Stochastic MPC of Constrained Linear Uncertain Systems
Chao Ning, Fengqi You
This paper investigates the problem of designing data-driven stochastic Model Predictive Control (MPC) for linear time-invariant systems under additive stochastic disturbance, whos…
Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions
Abdulelah S. Alshehri, Rafiqul Gani, Fengqi You
The optimal design of compounds through manipulating properties at the molecular level is often the key to considerable scientific advances and improved process systems performance…
Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems
Akshay Ajagekar, Fengqi You
Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis…
Quantum computing for energy systems optimization: Challenges and opportunities
Akshay Ajagekar, Fengqi You
The purpose of this paper is to explore the applications of quantum computing to energy systems optimization problems and discuss some of the challenges faced by quantum computers…
Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)
Shipu Zhao, Fengqi You
This paper presents a novel deep learning based data-driven optimization method. A novel generative adversarial network (GAN) based data-driven distributionally robust chance const…
Efficient Greenhouse Temperature Control with Data-Driven Robust Model Predictive Control
Wei-Han Chen, Fengqi You
Appropriate greenhouse temperature should be maintained to ensure crop production while minimizing energy consumption. Even though weather forecasts could provide a certain amount…
Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems
Akshay Ajagekar, Travis Humble, Fengqi You
Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. T…
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
Chao Ning, Fengqi You
This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization…
A Posteriori Probabilistic Bounds of Convex Scenario Programs with Validation Tests
Chao Shang, Fengqi You
Scenario programs have established themselves as efficient tools towards decision-making under uncertainty. To assess the quality of scenario-based solutions a posteriori, validati…
Robust Model Predictive Control of Irrigation Systems with Active Uncertainty Learning and Data Analytics
Chao Shang, Wei-Han Chen, Abraham Duncan Stroock +1
We develop a novel data-driven robust model predictive control (DDRMPC) approach for automatic control of irrigation systems. The fundamental idea is to integrate both mechanistic…
A Transformation-Proximal Bundle Algorithm for Multistage Adaptive Robust Optimization and Application to Constrained Robust Optimal Control
Chao Ning, Fengqi You
This paper presents a novel transformation-proximal bundle algorithm for multistage adaptive robust optimization problems. By partitioning recourse decisions into state and control…
A data-driven robust optimization approach to scenario-based stochastic model predictive control
Chao Shang, Fengqi You
Stochastic model predictive control (SMPC) has been a promising solution to complex control problems under uncertain disturbances. However, traditional SMPC approaches either requi…
Data-Driven Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era
Chao Ning, Fengqi You
A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty dat…
A Projection-Based Reformulation and Decomposition Algorithm for Global Optimization of a Class of Mixed Integer Bilevel Linear Programs
Dajun Yue, Jiyao Gao, Bo Zeng +1
We propose an extended variant of the reformulation and decomposition algorithm for solving a special class of mixed-integer bilevel linear programs (MIBLPs) where continuous and i…