FUTURE MANUFACTURING lating data , updating an AI model , observing what conditions it cannot predict well , and collecting more simulated data for those conditions . Using industry tools engineers can generate simulated data that mirrors real-world scenarios .
Approximating Complex Systems with AI
When designing algorithms that interact with physical systems , the idea is to create a simulation-based model that gives you the necessary accuracy to recreate the physical system your algorithms interact with .
The problem with this approach is that to achieve the “ necessary accuracy ” engineers have historically built high-fidelity models from first principles . AI comes into the picture here in that engineers can take that high-fidelity model of the physical system that they ’ ve built and approximate it with an AI model ( a reduced-order model ). In other situations , they might just train the AI model from experimental data , completely bypassing the creation of a physics-based model . The benefit is that the reduced-order model is much less computationally expensive than the firstprinciples model .
AI for Algorithm Development
Engineers in applications like control systems have come to rely more-and-more on simulations when designing their algorithms . In many cases , these engineers are developing virtual sensors , that attempt to calculate a value that is not directly measured from the available sensors using AI-based approaches . They use data to train an AI model that can predict the unobserved state from the observed states , and then integrate that AI model with the system .
In this case , the AI model is included as part of the controls algorithm that ends up on the physical hardware , which has performance / memory limitations , and typically needs to be programmed in a lowerlevel language .
Reinforcement learning ( RL ) takes this approach a step further . Rather than learning just the estimator , RL learns the entire control strategy . This has shown to be a powerful but building such a model requires an accurate model of the environment , which may not be readily available , as well as the computational power to run many simulations .
In addition , AI algorithms are increasingly used in embedded vision , audio and signal processing , and wireless applications .
The Future of AI for Simulation
Overall , as models and complexity grow , AI and simulation will become even more essential tools for an engineer . Industry tools have empowered engineers to optimize their workflows and cut their development time by incorporating techniques such as synthetic data generation , reduced-order modeling , and embedded AI algorithms for controls , signal processing , embedded vision , and wireless applications .
With the ability to develop , test , and validate models in an accurate and affordable way , before hardware is introduced , these methodologies will only continue to grow in use . l
Dr-Ing . Rainer Mümmler Principal Application Engineer The MathWorks GmbH
A neural network is used to estimate battery state-of-charge from current , voltage , and temperature measurements .
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