The rapid evolution of AI has been upending industries at an unprecedented rate. And in 2025, we are poised to witness another leap forward, as AI becomes integral to the fields of simulation and digital twin technologies. When working in tandem, these tools promise to not only streamline processes, but elevate predictive accuracy to new levels. Ultimately, they can fundamentally reshape sectors from aerospace to automotive.
At the heart of this transformation is AI’s ability to dramatically accelerate the simulation process. Traditional simulations – whether for designing a next-generation aircraft or optimising a factory floor – often require hours, or even days, to compute. AI, with its remarkable capacity to learn from real-world data and adapt iteratively, will cut this time down to mere seconds. And this step up in efficiency will empower engineers and decision-makers to model complex systems, and make real-time decisions, without sacrificing reliability.
AI’s role in enhancing predictive accuracy
Digital twins have already proven invaluable in predicting performance, diagnosing problems, and exploring optimisation opportunities. However, their true potential is unlocked when combined with AI. In real-world customer examples, the combination of simulation and AI techniques has led to digital twins that predict with 99% accuracy. This represents a huge step forward for industries where even the smallest errors can result in substantial costs or safety risks.
In aerospace, for example, digital twins are being used to monitor aircraft health, ensuring timely maintenance and enhancing safety. With AI integration, these twins will be able to process vast amounts of operational data and adapt their predictions in real-time, reducing the risk of unexpected failures. Similarly, in automotive engineering, AI-powered digital twins will enhance the design and testing of vehicles – from aerodynamics to battery performance in electric cars – and enable manufacturers to bring products to market faster.
AI meets physics
One of the key enablers of industries’ transformation is the hybrid modelling approach that combines AI with physics-based simulations. While AI excels at processing massive datasets and identifying patterns, its predictions can occasionally stray into areas unsupported by physical reality, otherwise known as AI ‘hallucinations’. This is where physics-based models act as a safety net and ensure that AI-generated insights remain grounded in the immutable laws of nature.
Consider the development of autonomous vehicles as one example. Training an AI model to simulate billions of miles of driving scenarios virtually is far faster, safer, and more cost-effective than physical testing. However, an AI model might predict a behaviour that violates the laws of physics, such as a vehicle accelerating instantaneously or cornering in an impossible manner. Physics-based simulations provide the necessary guardrails, ensuring these virtual tests produce reliable and actionable insights.
This synergy between AI and physics-based modelling is already bearing fruit. Engineers now use hybrid models to optimise everything from wind turbines to medical devices, enabling designs that are both innovative and practical. As these tools advance, they will unlock new opportunities across industries, transforming the way we design, manufacture, and operate complex systems.
Overcoming challenges
While the benefits of AI-driven simulation and digital twins are undeniable, challenges do remain; thankfully, there are also solutions. One concern is the reliability of AI predictions in high-stakes applications. An AI system predicting incorrect outcomes in sectors such as healthcare or aviation could have catastrophic consequences. To address this, rigorous validation processes and the aforementioned hybrid modelling approach are critical. These mechanisms ensure that AI predictions are both trustworthy and verifiable, providing decision-makers with confidence in their outcomes.
Another challenge lies in the quality and volume of data required to train AI models effectively. For industries that are transitioning to AI-driven workflows, establishing robust data pipelines and ensuring data accuracy are paramount. Investments in sensors, Internet of Things (IoT) infrastructure, and secure data management will be essential to fully realise the potential of AI and digital twins in 2025 and beyond.
The next step for innovation
As we look to the future, there’s no doubt that AI will play an increasingly influential role in simulation and digital twin technologies. Organisations are already experiencing the benefits of leveraging AI, so this combined approach is a natural next step. By enhancing existing simulation and digital twin tools, and introducing hybrid models, companies can both evolve their existing products and pave the way for exciting new innovations.
Prith Banerjee is Chief Technology Officer at Ansys
Onshore wind and grid queue targeted in 2030 energy plan
Where is the evidence that air quality is declining? On the contrary it has massively improved since the 1970´s <a...