September 2, 2025

Industry Signals - September 2, 2025: When AI Meets Simulation: Engineering’s Next Innovation Frontier

# AI

# CFDModeling

# Digital Manufacturing

# Digital Transformation

# Digital Twin

# Factory of the Future

# Industrial AI

# Innovation

# Siemens Xcelerator

# Smart Manufacturing

How the fusion of data-driven intelligence and physics-based precision is redefining product innovation, speed, and competitiveness.

Welcome to Industry Signals. This week, we are breaking down the powerful intersection of artificial intelligence and simulation, drawing on insights from the recent AI Spectrum podcast episodes "Understanding the Intersection of AI and Simulation,” part one and part two. In the conversation, Siemens experts Todd Tuttle (VP, Aerospace & Defense), Justin Hodges (AI/ML Technical Specialist), and Fatma Kocpinar (VP, Engineering Data Science at Altair) explore how AI and simulation are increasingly interdependent, offering faster, more insightful, and cost-effective product development cycles. For decades, engineers have relied on physics-based simulation to model everything from jet turbines to semiconductor chips. Such methods are precise but can be significant undertakings, demanding vast computational resources and time. Artificial intelligence, by contrast, offers speed but not always accuracy. Used together, the two technologies could transform the economics of engineering.

Simulation delivers precision, while AI delivers speed. Together, they create a loop of mutual reinforcement. As Fatma Kocpinar, VP of Engineering Data Science at Altair, put it: “We use AI to explore thousands of designs quickly, and simulation to validate and generate optimal datasets for model training. Each augments the other.”

This allows engineers to test thousands of design options virtually, compressing what once took weeks into minutes. “We have customers who used to simulate a scenario in half a day. Now, it takes seconds,” said Siemens’ Justin Hodges. “That opens the door to far more creative, high-performance outcomes.”

These capabilities are already influencing critical business functions—from quoting to compliance. “Some customers are using these models not just for design, but to generate fast, reliable quotations,” Kocpinar noted. “It gives them a strategic edge early in the sales process.”

Far from rendering engineers redundant, this technological pairing enhances their role. By automating repetitive analyses, AI frees specialists to focus on creative system-level design. Human expertise remains indispensable, however. Algorithms require careful training and interpretation. Left unguided, they risk producing elegant nonsense.

That’s why simulation remains indispensable. “ML models are great at trends and directional insights,” Hodges explained, “but for trust, precision, and regulatory compliance, high-fidelity simulation will always be necessary.”

The foundation, as so often in artificial intelligence, is data. High-quality training datasets, supplemented with synthetic data when real-world examples are scarce, are essential. “Better data beats fancier algorithms,” said Kocpinar. “If your dataset isn’t balanced, it doesn’t matter how deep your model is.”

This convergence is more than a technical curiosity. It represents a shift in how products are conceived, tested, and brought to market. The ability to design, validate, and refine digitally, at speed, compresses development cycles and reduces cost. That, in turn, could alter the competitive balance in industries where time-to-market is critical. Siemens’ Todd Tuttle framed it succinctly: “This isn’t about replacing simulation or replacing engineers—it’s about making both better. It's how we shrink design cycles and increase innovation velocity.”

Despite the buzz, foundation models won’t replace simulation anytime soon. “The idea that you’ll hand a foundation model to an engineer and skip simulation? That’s just not credible,” said Hodges. “What we’ll see are narrow, domain-specific AI surrogates—powerful but purpose-built.”

The convergence of AI and simulation is a technical evolution and strategic inflection point. For industries where speed, precision, and innovation define competitiveness, this pairing is the new frontier.

For more on the topic, you might also be interested in:

Innovations in system simulation and AI/ML applications in CAE from FISITA: This white paper provides a deep dive into how AI and machine learning are being applied in Computer-Aided Engineering.

Artificial Intelligence Index Report 2025 from Stanford University: This 400+ page report covers everything about AI, but specific to the topic of the intersection of AI and simulation, it details how the synergy is accelerating major scientific breakthroughs. Specific examples include:

Robotics modeling/simulating: pages 152-155

Self-driving cars modeling/simulating: pages 156-160

AI-agent-created data breach & financial loss simulations: pages 208-209

Industrial Foundation Model from Siemens: This page is full of resources about leveraging real-world, contextualized datasets to enhance productivity and technical compliance by addressing complex industrial use cases across the value chain. It also provides information on how you can help shape the future of Industrial AI.

That’s a wrap for this edition of Industry Signals. Have a report, use case, or event you'd like to see featured in an upcoming issue? Send a note via PM. We’re always looking to spotlight what’s shaping the future of industry and find recommendations from the Xcelerator Community especially valuable. Your insights and experiences continually shape Industry Signals.

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Industry Signals - September 2, 2025: When AI Meets Simulation: Engineering’s Next Innovation Frontier

How the fusion of data-driven intelligence and physics-based precision is redefining product innovation, speed, and competitiveness.

For more on the topic, you might also be interested in:

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