3 citations · 4 across the 7 of their papers we have counts for
8 papers
Self-Supervised Tuning of Low-Fidelity Optimization Models via High-Fidelity Feedback
Gonzalo E. Constante-Flores, André H. Quisaguano-Paredes, Asha Ramanujam +2
Low-fidelity (LF) optimization models are widely used in large-scale operations because they can be solved efficiently and reliably, but they often exhibit systematic errors relati…
DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers
Shraman Pal, Can Li
Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches. At the same time, these proble…
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…
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…
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…