The Simulation Revolution: How AI and ‘Reduced Order’ Models Are Unlocking Real-Time Engineering
Weeks. That’s how long it can take to run a single, high-fidelity physics simulation for complex systems. For engineers needing rapid answers – whether designing an electric vehicle battery or optimizing food storage – this delay is crippling. But a convergence of artificial intelligence, streamlined modeling techniques, and a shift towards app-based deployment is poised to shatter those bottlenecks, ushering in an era of real-time multiphysics simulation.
The Bottleneck: Why Simulations Take So Long
Modern engineering problems rarely involve a single physical phenomenon. Designing a modern jet engine, for example, requires modeling fluid dynamics, heat transfer, structural mechanics, and electromagnetic fields – a ‘multiphysics’ challenge. Accurately representing these interactions demands immense computational power and time. As noted in a recent review published in Procedia Computer Science, this can lead to design cycles measured in weeks, hindering innovation and responsiveness.
Surrogate Models: A Speed Boost Powered by Machine Learning
The core of the solution lies in ‘surrogate models.’ Instead of solving the full, complex equations every time, these models leverage machine learning to create a simplified, faster approximation. “You take your fully-fledged multiphysics model and compress it down into a compact format that’s quick to evaluate,” explains Bjorn Sjodin, senior vice president of product management at COMSOL. “You can evaluate these models instantaneously, whereas solving the full model could take 15 minutes.”
This isn’t about sacrificing accuracy. Surrogate models are trained on data generated by the full simulation, learning to predict behavior across a range of scenarios. COMSOL is already seeing real-world impact: European automotive manufacturers are using these models to rapidly simulate EV battery pack performance, while a Swiss institute has deployed a surrogate-based app to help Indian farmers predict and reduce food spoilage – achieving a 20% reduction in waste.
Beyond AI: The Power of ‘Reduced Order’ Modeling
While machine learning is a key enabler, it’s not the whole story. ‘Reduced order models’ (ROMs) employ a range of techniques to streamline simulations. Researchers at the International School for Advanced Studies in Trieste, Italy, highlight two main approaches: intrusive methods, which directly modify the governing equations, and non-intrusive methods, which analyze simulation data. These techniques, often used in combination with neural networks, can achieve speedups of up to 100,000x compared to traditional models.
ROMs work by identifying and eliminating unnecessary complexity. Mathematical pattern recognition and equation simplification are common tactics. The goal is to capture the essential behavior of the system without the computational overhead of a full-scale simulation. Think of it like creating a detailed map versus a simplified route planner – both get you to your destination, but one is far more efficient for everyday use.
From Simulation to Application: Democratizing Engineering Power
COMSOL is taking this a step further by enabling users to compile their surrogate models into standalone applications. These apps can run on laptops, smartphones, or even factory floor devices, bringing simulation power directly to those who need it most. This ‘appification’ of simulation removes licensing barriers and empowers a wider range of users to make data-driven decisions.
The Rise of the ‘Simulation App Developer’
Sjodin envisions a future where COMSOL users become “software developers in their own right,” creating custom simulation tools tailored to specific needs. This shift has the potential to unlock a wave of innovation, as engineers can rapidly prototype and test new designs without relying on centralized simulation teams.
Looking Ahead: The Future of Real-Time Engineering
The convergence of AI, ROMs, and app-based deployment is more than just a speed boost; it’s a fundamental shift in how engineering is done. We can expect to see:
- Increased adoption across industries: From aerospace and automotive to healthcare and energy, any field reliant on complex simulations will benefit.
- Edge computing for simulations: Running simulations directly on devices (the “edge”) will enable real-time control and optimization in remote or resource-constrained environments.
- Automated model creation: AI will play an increasingly important role in automatically generating and optimizing surrogate models, further reducing the time and expertise required.
The era of waiting weeks for simulation results is coming to an end. The future of engineering is real-time, accessible, and powered by intelligent simulations. What challenges do you foresee in implementing these technologies within your organization? Share your thoughts in the comments below!
