3 citations · 4 across the 9 of their papers we have counts for
9 papers
Self-Supervised Learning of Parametric Approximation for Security-Constrained DC-OPF
Anderson Anrrango, André Quisaguano, Gonzalo E. Constante-Flores +1
This paper introduces a self-supervised learning framework for approximating the Security-Constrained DC Optimal Power Flow (SC-DCOPF) problem using a parametric linear model. The…
Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
Gonzalo E. Constante-Flores, Hao Chen, Can Li
Deep learning models are increasingly deployed in safety-critical tasks where predictions must satisfy hard constraints, such as physical laws, fairness requirements, or safety lim…
OptiChat: Bridging Optimization Models and Practitioners with Large Language Models
Hao Chen, Gonzalo Esteban Constante-Flores, Krishna Sri Ipsit Mantri +3
Optimization models have been applied to solve a wide variety of decision-making problems. These models are usually developed by optimization experts but are used by practitioners…
A Quadratically-Constrained Convex Approximation for the AC Optimal Power Flow
Gonzalo E. Constante-Flores, Can Li
We introduce a quadratically-constrained approximation (QCAC) of the AC optimal power flow (AC-OPF) problem. Unlike existing approximations like the DC-OPF, our model does not rely…
FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
Abdullah Khan, Rahul Nahar, Hao Chen +2
Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes. However, existing data-driven FDD platforms often lack inte…
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators
Amirhossein Mollaali, Gabriel Zufferey, Gonzalo Constante-Flores +4
This paper proposes a new data-driven methodology for predicting intervals of post-fault voltage trajectories in power systems. We begin by introducing the Quantile Attention-Fouri…
AC-Network-Informed DC Optimal Power Flow for Electricity Markets
Gonzalo E. Constante-Flores, André H. Quisaguano, Antonio J. Conejo +1
This paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximatio…
Physics-Informed Neural Networks with Hard Linear Equality Constraints
Hao Chen, Gonzalo E. Constante Flores, Can Li
Surrogate modeling is used to replace computationally expensive simulations. Neural networks have been widely applied as surrogate models that enable efficient evaluations over com…
Diagnosing Infeasible Optimization Problems Using Large Language Models
Hao Chen, Gonzalo E. Constante-Flores, Can Li
Decision-making problems can be represented as mathematical optimization models, finding wide applications in fields such as economics, engineering and manufacturing, transportatio…