In today’s tech landscape, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as the hottest buzzwords, captivating the attention of both industry insiders and the general public alike. One primary reason for this fervent interest is the transformative potential these technologies hold across various sectors. AI and ML algorithms have revolutionized how businesses operate, enabling them to streamline processes, optimize decision-making, and unlock new avenues for innovation.

Leveraging Artificial Intelligence and Machine Learning (AI/ML) for Simulation

Artificial Intelligence (AI) designs systems that emulate human behavior through predefined rules, while Machine Learning (ML) allows computers to learn these rules from training data autonomously. Simulation, on the other hand, scrutinizes real-world phenomena using virtual models.

Although each concept operates independently, they synergize continuously. Simulations leverage AI and ML methodologies to streamline model execution, while AI and ML harness simulation techniques to generate synthetic data spanning diverse industries and applications.

Transformations in Simulation: How AI and ML are Shaping the Field

The engineering simulation field is experiencing significant transformations due to the progress of AI and ML technologies. These developments are refining numerical methodologies like finite element analysis (FEA), finite volume methods (FVM), and finite different time domains (FDTD), streamlining the resolution of complex 3D physics problems with increased speed and precision. These advancements not only boost solver efficiency but also introduce dynamic visualization capabilities, thereby enhancing the overall user experience in engineering simulation.

Here are some key transformations underway in engineering simulation:

Unleashing the Full Potential of AI and ML:

AI and ML are reshaping every aspect of our lives, and engineering simulation is no exception. Their limitless capabilities are quietly but significantly altering the landscape of the engineering simulation industry.

Identifying Recurring Patterns:

Machine learning algorithms excel at recognizing recurring patterns within geometries, allowing for efficient compression of essential information. Trained models can decode this compressed representation into complete 3D or 2D geometries when needed, enhancing efficiency in data representation.

An enormous amount of data can be stored:

Artificial Intelligence and machine learning are applied to computer vision and natural language processing, where extremely large, numerically intensive, complex problems can be solved. With unmatched ability, they can ingest huge volumes of data in training mode and deliver statistically accurate answers in deployment mode within milliseconds. Often, these simulations would take weeks or even months to complete if done by humans.

Enhancing Customer Usability:

Machine learning technologies play a vital role in categorizing geometries, identifying part connections, and serving as recommender systems for simulation setup. This enhances user-friendliness and output for customers, marking a notable improvement in the usability of simulation software.

The Parameters of Simulation:

AI/ML techniques can automatically determine simulation parameters to enhance both speed and precision concurrently. Through augmented simulation for training neural networks via data-driven or physics-informed approaches, we can expedite the simulation by a factor of 100X.

Pushing the Limits with MeshWorks AI/ML Technology:

MeshWorks AI/ML technology is a robust framework integrating a wide range of algorithms and models, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and approximation and response surface models. Its adaptability is a standout feature, with models continuously trained on customer data to ensure ongoing relevance and accuracy.

Moreover, the framework excels in fitting diverse AI/ML models to different responses, enhancing accuracy and prediction outcomes for varied scenarios. Additionally, it seamlessly links predictive models to parametric Computer-Aided Engineering (CAE) models, enabling visual parameters like automotive rail cross-sections to directly influence output responses such as crash pulses. This integration facilitates the creation of Design Advisor applications, offering users an immediate visual understanding of how geometric changes impact predicted responses, while also extending its utility to sensitivity analyses, optimization studies, and beyond, making it indispensable for a wide array of engineering tasks.

Advanced Update in the AI/ML MeshWorks module:

By integrating AI/ML capabilities, design iterations can be made in the early phases of product development. This integration enables training and generation of predictive information tailored to specific needs. For instance, when predicting parameters such as CFD, NVH, durability, and crash analysis, the module possesses built-in functionality to incorporate changes and predict outcomes swiftly.

Furthermore, a key feature of the module is its ability to generate models with just geometric data. The software seamlessly handles numerous neural networks without crashing, ensuring that the more neural networks are utilized for different models, the more accurate the predictions become. Once the data is uploaded, the process becomes a one-time effort, providing ongoing benefits.