13 papers
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…