PhysicsX demonstrates that large physics models trained on simulation and real-world data can supersede traditional CAE, enabling high-fidelity simulation across the entire product lifecycl…

The full data center stack — gas turbines, chilling units, step-down transformers, and internal equipment — represents a massive industrial surface area where AI-driven simulation can optim…

Established data and model-size scaling laws for physics models — where larger models trained on more data generalize better zero-shot — give PhysicsX conviction to invest heavily in larger…

A new wave of neo-manufacturers across semiconductors, quantum, fusion, and aerospace are reaching production scale and becoming major engineering organizations, creating a growing customer…

PhysicsX's work with semiconductor equipment manufacturers and fabs on advanced packaging and process control indicates the semi supply chain is a beachhead for AI-native simulation, with g…

While European policy and industry rhetoric supports working with AI-native startups, commercial adoption and spend rates remain materially lower than in the US and Asia, creating a geograp…

The compute, data, and model-size scaling laws PhysicsX has established for physics models parallel those in robotics for embodiment-free control, suggesting a shared trajectory for AI mode…