Latest Articles for Reduced-Order Models

This study focuses on structural monitoring in fusion devices under various forces.

GFN-ROM enhances model order reduction using graph neural networks for efficient simulations.

This study presents a method to improve fluid-structure interaction predictions for engineering applications.

A new framework enhances predictive modeling by integrating sensor and simulation data.

Tracking movement through videos simplifies the study of complex systems.

Examining reduced-order modeling and neural networks in wave propagation calculations.

Effective methods for understanding random forces in mechanical systems.

A look at non-linear behaviors and reduced-order models in aircraft design.

Learn how reduced-order models aid in analyzing complex systems using causation entropy.

ROMs streamline complex simulations for efficient engineering solutions.

A new approach for nonlinear systems enhances modeling efficiency and accuracy.

New modeling techniques enhance rocket propulsion systems through efficient data analysis.

A new method enhances reduced-order models using Bayesian learning and Gaussian processes.

New techniques improve efficiency in modeling complex systems.

Combining domain decomposition and operator inference for faster simulations.

Streamlining digital twins through reduced order models and high-performance computing.

Researchers create simpler models to predict complex fluid behaviors efficiently.

Research on reduced models for predicting behaviors of bolted joints under dynamic loads.

Methods to enhance efficiency in simulating complex turbulent flows.

Learn about space-time model reduction to analyze complex systems efficiently.

New methods improve the analysis of aerospace structures for better performance.

Learn how predictive control enhances robot safety in complex environments.

A look into optimal control methods for managing transport systems effectively.