Introducing physics AI at Mistral: the foundation for engineering acceleration.

Physics AI research that’s shaping the industry.

Published breakthroughs pushing the state of the art.

Physical AI: What It Is and What It Is Not

A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science .

Mistral AI raises 1.7B€ to accelerate technological progress with AI

Introducing Mistral AI Studio.

Hallo, Deutschland!

Mistral AI has announced the opening of a new hub in Munich dedicated to Physics AI and Industrial AI research. The facility will collaborate closely with German industry partners to advance AI applications in these fields.

Bringing open AI models to the frontier

Why we're building Mistral AI.

NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets

Discovering new solutions to century-old problems in fluid dynamics

Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.

Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineering

MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.

The Past and Future of AI Scientists

arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.

Towards Trustworthy Physical AI: From Theory to Practice Across Life Cycle

The paper introduces the concept of Physical AI—systems that understand and act within the physical world, where interactions are continuous, uncertain, and irreversible. It surveys trustworthy principles specific to Physical AI, outlines the role of physics in AI, and maps the end‑to‑end life cycle across five core stages, culminating in the Trustworthy Physical AI Operationalization (T‑PAIO) and the broader Trustworthy Physical AI (T‑PAI) framework.

Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough

arXiv:2607. 10039v1 Announce Type: cross Abstract: Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing.