6 papers
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…
Optimal Reactive Operation of General Topology Supply Chain and Manufacturing Networks under Disruptions
Daniel Ovalle, Joshua L. Pulsipher, Yixin Ye +4
Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unp…