Tag: machine learning simulation

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Simulation for the HVACR Industry: How to Leverage Machine Learning

The HVACR Industry is Evolving As we step into 2025, major trends in systems simulation are emerging. The heating, ventilation, air conditioning, and refrigeration (HVACR) industry is seeing accelerated growth in the adoption of simulation throughout the design and development process. This industry is looking to further modernize, appeal to consumers, and demand energy-efficient, sustainable […]

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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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Optimizing Neural Networks for Modeling and Simulation (Machine Learning Blog Part 2)

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 Blog 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) […]

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hvac machine learning simulation|machine learning simulation in hvac industry|EV thermal system model with physics-based solution and feedforward neural net

Machine Learning Simulation: HVACR Industry

The HVACR Industry Is Evolving To Meet Climate Change Needs Recently our colleagues from Gamma Technologies (GT) attended the 50th Herrick Conferences at Purdue University. These conferences occur bi-annually and cover a span of areas that are important to the heating, ventilation, air conditioning, and refrigeration (HVACR) industry including: pumps and compressors, refrigeration and air […]

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