Tag: ai

accessory drive machine learning simulation|dynamic machine learning simulation|neural network structure|predicted vs. target stored ammonia coverage and NO outlet mass flow rate|dynamic and static neural networks|metamodel predictions of battery thermal performance simulation|battery call model simulation|Transient neural network predictions of voltage and state of charge (SOC)|neural network structure|machine learning simulation|||neural network and a linear interpolating lookup map|doe simulation|||machine learning neural networks simulation|GT-SUITE accessory drive model|Torsional and Transverse Vibration in an Accessory Drive|belt tension and global slip simulation|Main Effects Plots for Specified Attributes or Inputs Ranking|Variational Analysis for Specified Attributes|belt tension and global slip simulation

Leveraging Machine Learning for Early Design Decisions on an Accessory Belt Drive Simulation

There are various challenges faced by an automotive engineer while designing a robust and optimized accessory drive system. Most original equipment manufacturers (OEMs) rely on different suppliers for their engine belt(s) and accessories (e.g. water pumps, alternators, A/C compressors, etc.). This leads to challenges in obtaining a comprehensive set of input data to incorporate in […]

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dynamic machine learning simulation|machine learning neural networks simulation|||||neural network and a linear interpolating lookup map|doe simulation|machine learning simulation|neural network structure|Transient neural network predictions of voltage and state of charge (SOC)|battery call model simulation|metamodel predictions of battery thermal performance simulation|dynamic and static neural networks|predicted vs. target stored ammonia coverage and NO outlet mass flow rate|neural network structure

Dynamic Machine Learning for Modeling and Simulation

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 […]

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machine learning neural networks simulation|machine learning simulation|doe simulation|neural network and a linear interpolating lookup map||||

Optimizing Neural Networks for Modeling and Simulation

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 […]

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machine learning simulation|neural network and a linear interpolating lookup map|doe simulation

Enhancing Model Accuracy by Replacing Lookup Maps with Machine Learning Models

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) […]

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