13 citations · 14 across the 16 of their papers we have counts for
19 papers
A Latent Space Optimization Approach for Symbolic Discovery of Dynamical Models
Tongjia Liu, Ilias Mitrai
In this paper, we propose a hybrid latent-space Bayesian Optimization (BO) and global optimization framework for solving symbolic regression tasks to discover dynamical models from…
Learning to Explain Solutions of Optimal Control Problems
Jiyong Lee, Ilias Mitrai
In this paper, we propose an explainable artificial intelligence framework to explain the solution of optimal control problems predicted with graph neural networks (GNNs). The prop…
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…
Approximate Dynamic Optimization via Deep Neural Operators
Amin Nassaji, Ilias Mitrai, Prodromos Daoutidis
This paper addresses the solution of nonlinear dynamic optimization problems that compute optimal manipulated input profiles to enforce desired output profiles. Such trajectory opt…
Symbolic Discovery of Iterative Algorithms: A Continuous Latent Space Bayesian Optimization Framework
Tongjia Liu, Ilias Mitrai
In this paper, we consider the automated discovery of iterative optimization algorithms. We formulate the algorithm discovery task as a discrete optimization problem and search for…
Industrial electrification in the era of data centers: A Bayesian Optimization approach for grid-aware large load allocation
Jiyong Lee, Erhan Kutanoglu, Michael Baldea +1
Large loads from industrial electrification and data centers are reshaping the planning and operation of the power grid. Identifying optimal large load siting decisions while accou…
A constrained symbolic regression approach for Lyapunov function discovery
Ilias Mitrai, Wentao Tang
In this paper, we consider the data-driven discovery of Lyapunov functions for autonomous dynamical systems. We represent the Lyapunov function as an expression tree of fixed depth…
Grid Capacity Expansion under Data Centers and Electrified Manufacturing Large Loads
Jiyong Lee, Melody Agustin, Joanne Langsdorf +3
In this paper, we consider the expansion of power grids under emerging large loads from data centers and electrified manufacturing. We develop a multi-period grid capacity expansio…
Learning regime-dependent governing equations: A symbolic decision tree approach
Ilias Mitrai, Tongjia Liu, Gabriel E. Sanoja
Many chemical engineering systems are governed by mechanisms that switch across operating regimes, making the data-driven discovery of regime-dependent governing equations essentia…
A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
We propose a hybrid reinforcement and self-supervised learning framework for accelerating generalized Benders decomposition (GBD). In this framework, a graph based reinforcement le…
Learning interpretable and stable dynamical models via mixed-integer Lyapunov-constrained optimization
Zhe Li, Ilias Mitrai
In this paper, we consider the data-driven discovery of stable dynamical models with a single equilibrium. The proposed approach uses a basis-function parameterization of the diffe…
Feasibility-Aware Imitation Learning for Benders Decomposition
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
Mixed-integer optimization problems arise in a wide range of control applications. Benders decomposition is a widely used algorithm for solving such problems by decomposing them in…
Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
This work introduces an end-to-end graph-based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural n…
Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
Ilias Mitrai
In this paper, we propose symbolic decision trees as surrogate models for approximating model predictive control laws. The proposed approach learns simultaneously the partition of…
Learning to control inexact Benders decomposition via reinforcement learning
Zhe Li, Bernard T. Agyeman, Ilias Mitrai +1
Benders decomposition (BD), along with its generalized version (GBD), is a widely used algorithm for solving large-scale mixed-integer optimization problems that arise in the opera…
Accelerating process control and optimization via machine learning: A review
Ilias Mitrai, Prodromos Daoutidis
Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution alg…
Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize
Ilias Mitrai, Prodromos Daoutidis
The repeated solution of large-scale optimization problems arises frequently in process systems engineering tasks. Decomposition-based solution methods have been widely used to red…
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose
Ilias Mitrai, Prodromos Daoutidis
In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition-based solution method. In this approach, an o…
Computationally efficient solution of mixed integer model predictive control problems via machine learning aided Benders Decomposition
Ilias Mitrai, Prodromos Daoutidis
Mixed integer Model Predictive Control (MPC) problems arise in the operation of systems where discrete and continuous decisions must be taken simultaneously to compensate for distu…