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Found 10 papers, 0 papers with code
Generative AI and Process Systems Engineering: The Next Frontier
This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE).
Online Learning Based Risk-Averse Stochastic MPC of Constrained Linear Uncertain Systems
We propose a novel online learning based risk-averse stochastic MPC framework in which Conditional Value-at-Risk (CVaR) constraints on system states are required to hold for a family of distributions called an ambiguity set.
Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions
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
Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years.
Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)
A novel generative adversarial network (GAN) based data-driven distributionally robust chance constrained programming framework is proposed.
Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems
The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
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 that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities.
A Posteriori Probabilistic Bounds of Convex Scenario Programs with Validation Tests
By synthesizing comprehensive information about support constraints and validation tests, improved risk evaluation can be achieved for randomized solutions in comparison with existing a posteriori bounds.
Robust Model Predictive Control of Irrigation Systems with Active Uncertainty Learning and Data Analytics
For evapotranspiration forecast error, the support vector clustering-based uncertainty set is adopted, which can be conveniently built from historical data.
Data-Driven Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era
A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure.
