Laurent Di Valentin, Enric Aramburu, and Ernesto Mottola, representing the FISITA Digitalisation Expert Group, consider recent developments in system simulation, and the artificial intelligence and machine learning applications that are shaping the future of computer aided engineering.
System simulation, and the use of artificial intelligence (AI) and machine learning (ML) applications in computer-aided engineering (CAE), are two topics which have introduced new and evolving complexities and technological advancements in the automotive industry. To address these critical topics, the FISITA Digitalisation Expert Group is developing two white papers which explore real-world and potential applications, and discuss considerations for engineering teams considering the adoption of these technologies in their work.
The shift towards software-defined vehicles and the role of system simulation
The rapid evolution of software-defined vehicle (SDV) technology is introducing a significant transformation in the global automotive industry. This shift is driven by the increasing integration of embedded electronics and software, which are becoming key value elements in modern vehicles. Today’s vehicles are increasingly software-driven, requiring the creation of cohesive hardware/software platforms that integrate all vehicle systems. This transition is particularly evident in electric vehicles, advanced driver-assistance system (ADAS), and connected technologies, where the “experience” often takes precedence in customer choice criteria.
Given this shift, system simulation has become a cornerstone in the automotive industry. It allows for the virtual development, testing, and validation of vehicle components and systems before physical prototypes are built. This capability significantly reduces development time and costs, enabling engineers to identify potential issues early in the design process. For automakers and automotive suppliers, system simulation provides a competitive edge by optimising vehicle performance, safety, and efficiency, which are critical in the highly competitive automotive market.
Unlike traditional mechanical systems, today's vehicles are digital native products, offering personalised digital services and enhanced user experiences
System simulation helps manage this complexity by allowing engineers to model and test these systems in a virtual environment. This is essential for ensuring regulatory compliance, reducing costs, and enhancing collaboration among automakers, suppliers, and software tool vendors. While system simulation is closely linked to system development methodologies such as Model-Based Systems Engineering (MBSE), our system simulation white paper will focus specifically on simulation at the logical and physical levels. It will provide a detailed definition of different levels of XiL digitalisation and present concrete use cases from various areas.
An overview of this topic will be provided during the Digitalisation & AI session at the 2025 FISITA World Mobility Conference in Barcelona.
AI and ML transformations in CAE
AI and ML are transforming CAE in the automotive industry. These technologies address the limitations of traditional CAE processes, such as long simulation times and high computational costs. AI and ML enable faster, more accurate simulations and optimise design parameters based on historical data.
Several AI/ML techniques are being integrated into CAE for key applications, notably predictive simulation, instantaneous evaluation and generative optimisation, and model generation and post-processing algorithms.
Looking ahead, several trends and challenges are emerging, such as real-time digital twins, and the increased use of reinforcement learning to dynamically optimise design processes
Predictive simulation plays a crucial role in expediting crash simulations, aerodynamics testing, and material performance analysis, thereby reducing the need for physical prototypes; instantaneous evaluation and generative optimisation, achieved through deep learning surrogate models, allow for near real-time simulations, which are particularly beneficial in managing quick iterations for optimisation; and model generation and post-processing algorithms support the quality process of CAE models, from their generation to the analysis of the simulations.
Our white paper will include several case studies demonstrating the practical applications of these technologies in different fields, such as crashworthiness, aerodynamics, and manufacturing processes.
Looking ahead, several trends and challenges are emerging, such as real-time digital twins—in which the growing role of digital twin technology will enable real-time performance monitoring—and the increased use of reinforcement learning to dynamically optimise design processes, especially for evolving vehicle safety standards.
About the authors
- Laurent Di Valentin is CAE Senior Fellow at Stellantis and Chair of the FISITA Digitalisation Expert Group;
- Enric Aramburu is Product Manager, Fluid Engineering at Applus+ IDIADA;
- Ernesto Mottola is Senior Manager, Information Technology and Digital for R&D and Purchasing at Toyota Motor Europe.
These white papers will be published by the FISITA Digitalisation Expert Group in mid-2025.
At the 2025 FISITA World Mobility Conference, Laurent Di Valentin and Ernesto Mottola will present the key findings of the FISITA Digitalisation Expert Group, in a 30-minute keynote highlighting the group’s work, with a focus on system simulation.