Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization.

Comprehensive Analysis on Machine Learning Approaches for Interpretable and Stable Soft Sensors.

Deep Learning-Based Approximation of Model Predictive Control Laws Using Mixture Networks.

Stable predictive control of continuous stirred-tank reactors using deep learning.

Adaptive Principal Components Allocation with the ℓ_2,g-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models.

Can a Single Tree Outperform an Entire Forest?

Causality-inspired Latent Feature Augmentation for Single Domain Generalization.

Simultaneous digital twin identification and signal-noise decomposition through modified generalized sparse identification of nonlinear dynamics.

High-Order Tensor Recovery with A Tensor U₁ Norm.

A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets.

A Global Optimization Algorithm for K-Center Clustering of One Billion Samples.

An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large-scale optimization applications.

Optimization of compressor standby schemes for gas transmission pipeline systems based on gas delivery reliability.

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization.

Deep Learning-based Predictive Control of Battery Management for Frequency Regulation.

Using stochastic programming to train neural network approximation of nonlinear MPC laws.

Supply reliability assessment of a gas pipeline network under stochastic demands.

Multi-objective transient peak shaving optimization of a gas pipeline system under demand uncertainty.

A scalable global optimization algorithm for stochastic nonlinear programs.

Graph-based modeling and simulation of complex systems.

Scalable modeling and solution of stochastic multiobjective optimization problems.

Clustering-based preconditioning for stochastic programs.

An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units.

An interior-point method for efficient solution of block-structured NLP problems using an implicit Schur-complement decomposition.