Incorporating Dynamic Metamodeling Simulation To save computational time, engineers are persistently trying to speed up physical models, and some situations absolutely require faster simulation speeds. These situations might include more advanced co-simulation tasks, performing model-based optimization on a slower physical model, or the need to have a surrogate model for XiL (X-in-the-Loop) applications or to […]
Why Neural Networks are Effective in Machine Learning Neural networks are powerful machine learning [ML] models that can capture highly nonlinear relationships between inputs and outputs within a dataset while being computationally inexpensive to execute. The benefits of neural networks for modeling and simulation activities, using the simulation platform GT-SUITE, were covered in part 1 […]
Machine Learning and Modeling Simulation Machine learning [ML] models, such as neural networks and other types of metamodels, are fast-executing mathematical representations of data that serve a variety of modeling and simulation purposes, including: Replacing computationally expensive physics-based sub-systems in integrated simulation models (for instance, we are using GT-SUITE simulation models for the HVACR industry) […]
What is Sensitivity Analysis? Sensitivity analysis is the study of how uncertainty in a model output (or response) can be attributed to different sources of uncertainty in the model inputs (or factors). These analyses are powerful tools for modeling and simulation activities, as they allow the modeler to identify the most and least influential parameters […]
Simulating Battery Packs – Not All Cells are the Same When simulating a large battery module, typically we assume that all the cells in the module are going to be the same. However, that is not always the case. Factors such as the capacities and resistances of the cell can vary from cell-to-cell. This brings […]
