If AI and ML fascinate you, you need to check out this mix of AI, ML, and engineering simulations
There is no denying that artificial intelligence and machine learning are changing the world. AI and ML are forcing us to see the world in a way that was not even imagined. But can anyone think of mixing engineering simulation with AI and ML.
AI and ML are changing engineering simulation. The article lists 10 ways through which AI and ML are changing engineering simulation.
AI and ML capabilities have no limits
Engineering simulation is no exception to how artificial intelligence and machine learning are affecting almost every aspect of our professional and personal lives.
It should come as no surprise that AI and ML are quietly changing the engineering simulation industry as there seems to be no limit to their potential.
by generating numerically accurate results
Ansys-Stanford team is harnessing new, data-driven and physics-informed machine-learning models that enable computer-aided design (CAD) engines to rapidly express simple forms through a new geometry encoding method Capitalizing on Convolutional Neural Networks.
The output of the new, less resource-intensive encoding technique is reduced spatial representation that nevertheless gives numerically accurate results.
by identifying repeating patterns
Machine learning can encode only the important information by finding recurring patterns in the geometry, allowing a respectable level of compression when representing the geometry. When necessary, a trained model can be used to decode this representation back into full 3D or 2D geometry.
by improving customer utility
When working with geometric parts and assemblies or setting up simulation challenges, machine-learning techniques can be used to classify geometry, detect part connections, and act as a recommender system to decide next steps can be done. This can mark a significant improvement in user-friendliness and output for customers.
Making simulations faster and smarter
The promise of artificial intelligence and machine learning to transform the world as we know it – including the capabilities of simulation software – has not yet been realized on a global scale as the applications of these technologies are still in their relative infancy.
Being used by a wide range of industries
But a vast number of consumers and industries are successfully implementing AI/ML. Financial algorithmic trading, sentiment research, and the ability for e-commerce owners to tailor their services to online shoppers have all been made possible by this technology. Investors can get profit on stock trading opportunities (recommendation engine).
Finding the Parameters of the Simulation
To increase speed and accuracy simultaneously, simulation parameters can be found automatically using AI/ML techniques. By using augmented simulation to train neural networks using data-driven or physics-informed methods, we can speed up simulations by a factor of 100X.
AI/ML Improve Customer Productivity
It can also increase customer productivity. The chip integrates high-fidelity solutions with ML approaches to locally coarse regions by accelerating thermal solutions and making fluid solvers can enhance AI and ML simulations.
driving intelligent business decisions
This can affect business intelligence options such as resource forecasting requirements for our solvers. It can combine simulation- and data analytics-based digital twins, to create accurate and quick hybrid digital twins.
Bridging the gap between the ideal world and the real world
The gap between the ideal world—where time, effort, efficiency, and results are perfectly balanced—and what actually happens in the real world can be bridged with the help of AI/ML. This will allow us to minimize the trade-off between simulation productivity, applicability and accuracy.