20 citations · 71 across the 16 of their papers we have counts for
16 papers
Solution Methods for Infinite-Dimensional Generalized Disjunctive Programming
Daniel Nguyen, Joshua L. Pulsipher
Generalized disjunctive programming (GDP) expresses mixed discrete-continuous decisions through Boolean indicators and disjunctions, and can be systematically solved via a library…
PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projection
Fateme Mohammad Mohammadi, Hector Budman, Joshua L. Pulsipher
While physics-informed neural networks (PINNs) have shown strong potential for process modeling, physical equations are only enforced as soft constraints during training, and thus,…
GPU-Accelerated Direct Transcription-Based Nonlinear Model Predictive Control
Evelyn Gondosiswanto, Joshua L. Pulsipher
In this paper, we present a GPU-accelerated framework for nonlinear model predictive control (NMPC) based on direct transcription and second-order interior-point methods. Many real…
A Digital Twin Simulator of a Pastillation Process with Applications to Automatic Control based on Computer Vision
Leonardo D. González, Joshua L. Pulsipher, Shengli Jiang +2
We present a digital-twin simulator for a pastillation process. The simulation framework produces realistic thermal image data of the process that is used to train computer vision-…
Event Constrained Programming
Daniel Ovalle, Stefan Mazzadi, Carl D. Laird +2
In this paper, we present event constraints as a new modeling paradigm that generalizes joint chance constraints from stochastic optimization to (1) enforce a constraint on the pro…
A Comparison of Strategies to Embed Physics-Informed Neural Networks in Nonlinear Model Predictive Control Formulations Solved via Direct Transcription
Carlos Andrés Elorza Casas, Luis A. Ricardez-Sandoval, Joshua L. Pulsipher
This study aims to benchmark candidate strategies for embedding neural network (NN) surrogates in nonlinear model predictive control (NMPC) formulations that are subject to systems…