Top 10 Ways in which AI And ML Are Changing Engineering Simulation
If AI and ML intrigue you, you need to see this mix of AI, ML and Engineering Simulation
There is no denying the fact that Artificial Intelligence and Machine learning are changing the World. AI and Ml are making us look at the world in a way it was not imagined. But could one think of Engineering Simulation mixed 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.
No limits to AI and ML Capabilities
Engineering simulation is not an exception to how artificial intelligence and machine learning are influencing virtually every aspect of our professional and personal life.
It shouldn’t be surprising that AI and ML are silently transforming the engineering simulation industry given that they appear to have no bounds on their potential.
By producing Numerically Accurate Results
The Ansys-Stanford team is exploiting new, data-driven, and physics-informed machine-learning models that could enable computer-aided design (CAD) engines to swiftly express simple forms via a new geometry encoding method, capitalising on convolution neural networks.
The outputs of the new, less resource-intensive encoding technique reduced spatial representation that nonetheless yields numerically precise results.
By Identifying Repeating Patterns
Machine learning can encode only important information by spotting recurring patterns in geometries, allowing for a respectable level of compression when representing geometries. When necessary, a trained model can be utilised to decode this representation back into whole 3D or 2D geometry.
By Improving Customer Usability
When working with geometric parts and assemblies or setting up simulation challenges, machine-learning technologies can be used to categorise geometries, detect part connections, and function as a recommender system to decide next steps. This may mark a significant improvement in user friendliness and output for customers.
By making simulation Faster and smarter
The promise of artificial intelligence and machine learning to change the world as we know it — including the capabilities of simulation software — has yet to be realised on a global scale since the applications of these technologies are still in their relative infancy.
Being used by broad range of industries
But an expanding 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 are all made possible by this technology. Investors can gain an advantage over stock trade opportunities (recommendation engines).
Finding the Parameters of Simulation
To simultaneously increase speed and accuracy, simulation parameters can be automatically found using AI/ML techniques. By using augmented simulation to train neural networks using data-driven or physics-informed methods, we can accelerate the simulation by a factor of 100X.
AI/ML improving Customer Productivity
It may even increase client productivity. By accelerating chip thermal solutions and creating the fluid solver that integrates high-fidelity solutions in local regions with ML approaches in coarse regions, AI and ML can enhance simulation.
By Driving Business intelligent decisions
It can influence business intelligence choices like resource forecasting requirements for our solvers. To build precise and quick hybrid digital twins, it can combine simulation- and data analytics-based digital twins.
Narrow the Gap between ideal world and real world
The difference between the ideal world—where time, effort, efficiency, and outcomes are perfectly balanced—and what really occurs in the real world can be reduced with the aid of AI/ML. It will allow us to lessen the trade-off between simulation productivity, usability, and accuracy.
