This release expands the role of AI across design and simulation, combining physics with machine learning speed. Generative design algorithms automatically explore optimal geometries, while predictive models accelerate decision-making with near-real-time feedback. Reduced order modeling (ROM) can now be run on GPUs for faster training of larger, more complex systems. An enterprise cloud solution leveraging geometric deep learning and on-demand HPC, enables teams to develop and deploy physics-based AI models in a secure, collaborative browser workspace, producing predictions up to 1,000x faster than traditional solvers without hardware constraints.

Performance improvements in pre-processing, particularly during model build and assembly, enable engineers to work confidently with the largest and most complex assemblies. These performance gains unlock new efficiencies in industries where speed and scale are critical, from automotive BIWs to aerospace structures and heavy equipment. Operations on production-scale models, such as box trim, move, split, undo/redo, and midsurface creation, are now dramatically faster, delivering smoother, near-instant workflows. Direct integration with PDM systems and batch meshing accelerates model assembly, while connector scenario management and representation controls ensure consistent mesh and connector standards across teams. Enhanced browsers and editors, including a new create entity dialog, attribute tables, and consolidated entity editor, streamline navigation and editing for complex assemblies.

Multiphysics integration continues to advance across structural, electromagnetic, thermal, and fluid domains, enabling engineers to analyze coupled systems more efficiently. Improvements across solvers deliver higher accuracy, including new electromagnetic workflows for e-motor and busbar analysis, structural–acoustic optimization, and thermal–fluid coupling for high-temperature and battery safety studies. Expanded interoperability and co-simulation standards allow transient, nonlinear, and high-impact scenarios to run more consistently across domains, strengthening digital continuity from concept through validation.

Automation and customization continue to transform simulation into a faster, more collaborative process. Expanded Python and API support, along with intuitive no-code workflow tools, remove repetitive work and let engineers customize processes to their needs. Enhanced visualization and plotting make results easier to interpret and share, promoting collaboration across teams and disciplines. Seamless integration with cloud platforms, co-simulation standards, and third-party software keeps data consistent and connected. Together, these advances strengthen interoperability and digital-twin fidelity, creating a smarter, faster, and more scalable simulation ecosystem.