The CAE Automation Myth – Why AI Won’t Replace Simulations Anytime Soon

The Hype vs. Reality of AI-Driven CAE Workflows

In today’s fast-paced engineering world, the idea of Artificial Intelligence (AI) completely replacing traditional Computer-Aided Engineering (CAE) simulations is an exciting yet misleading promise. Many companies believe AI can automate every aspect of simulation, cutting down time and costs drastically. But is this really possible?

While CAE automation and AI in simulation are transforming workflows, the reality is that AI cannot fully replace physics-based simulations like Finite Element Analysis (FEA) or Computational Fluid Dynamics (CFD). Instead, the real value of AI lies in advancing traditional CAE tools—improving efficiency, reducing manual work, and optimizing designs faster.

Let’s break down the three biggest challenges preventing AI from fully automating simulations and explore how engineers can realistically integrate AI-driven automation today.

Overestimating AI’s Role in CAE

Many CAE teams, previous posts and decision-makers be misled of expecting too much from AI. The assumption is that machine learning models can completely replace simulations, leading to:

• Instant results instead of time-consuming computations

• Reduced engineering effort without compromising accuracy

• Lower costs with no need for expensive simulation software

However, the reality is quite different. CAE automation is not the same as full AI-driven simulation replacement. While AI can help accelerate certain tasks, expecting it to entirely replace physics-based simulations is a dangerous misconception.

Why AI Can’t Replace CAE Simulations (Yet)

The hype around AI-driven engineering simulation automation often ignores three key limitations:

1. Data Limitations in AI Simulations

AI requires massive amounts of high-quality data to predict outcomes accurately. However, most CAE problems involve complex physics, unique materials, and boundary conditions that lack enough historical data for AI to generalize. Without extensive training data, AI-driven design validation remains unreliable.

2. The Complexity of Physics-Based Simulations

Simulations like FEA, CFD, and thermo-mechanical testing involve multiple interdependent parameters that cannot be captured by simple machine learning models. While AI can predict trends, it cannot replicate first-principle physics like heat transfer, structural deformation, or fluid flow with high fidelity.

3. Trust and Verification Challenges

Engineers rely on proven and validated results from traditional CAE methods. AI-generated predictions, however, often lack transparency. Without interpretability, it becomes difficult to trust AI-driven product development decisions—especially in safety-critical industries like aerospace and automotive.

These challenges highlight why AI can enhance but not replace CAE simulations.

AI-Enhanced CAE Workflows for Maximum Efficiency

Rather than expecting AI to replace CAE, the smart approach is to leverage AI for workflow automation and optimization. By strategically integrating AI-enhanced CAE tools, engineers can:

• Automate repetitive tasks (e.g., meshing, preprocessing, post-processing)

• Use AI-driven design optimization to explore multiple design variations rapidly

• Enhance material modeling by predicting material behavior under different conditions

• Speed up test data evaluation and generate material cards for simulation

• Reduce simulation setup times while still relying on physics-based solvers

This balanced approach allows engineering teams to reduce development time, cut costs, and increase productivity—without falling into the AI hype trap.

Conclusion: The Future of CAE and AI

The future of computational engineering with AI is promising, but full automation is not the answer—but the strategic integration is. AI is a tool when used correctly, can accelerate workflows and optimize processes. However, physics-based simulations will continue to be the foundation of accurate engineering analysis.

Instead of chasing the myth of full AI-driven CAE, engineers should focus on where AI can realistically help today: automating tedious tasks, speeding up design iterations, and enhancing existing workflows.

--> Want to explore how AI can transform your CAE workflow? Let’s discuss the future of automation in engineering!