--- title: Physics AI research that’s shaping the industry. link: https://mistral.ai/news/physics-ai-research/ source: mistral-ai published: 2026-05-27T12:00:05Z updated: 2026-05-27T12:00:05Z first_seen: 2026-10-19T01:26:53.998049939Z summary: Published breakthroughs pushing the state of the art. content: extracted html: 2026-05-27-physics-ai-research-that-s-shaping-the-industry.html remote_preview: url: https://mistral.ai/cms-media/api/media/file/Thumbnail-Manufacturing-Robotic%20Arm.jpg --- The acquisition of [Emmi AI](https://www.emmi.ai/) has highlighted Mistral’s commitment towards pushing the state-of-the-art in AI research and enterprise solutions for industrial engineering. Emmi’s work, now part of Mistral, is dedicated to fundamentally enabling engineers to build the next generation of  products faster and secure continuous performance gains in operations at scale for their customers. We are doubling down on building foundational Physics AI for the industries that shape the physical world, such as aerospace, automotive, semiconductors, and energy. Below are some of the published breakthroughs that this work rests on. DEC 1, 2025 #### Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes Existing aerospace datasets predominantly focus on 2D airfoils, neglecting these critical 3D phenomena. To address this gap, we present a new dataset of CFD simulations for 3D wings in the transonic regime. The dataset comprises volumetric and surface-level fields for around 30,000 samples with unique geometry and inflow conditions. [arXiv](https://arxiv.org/abs/2511.21474) NOV 25, 2025 #### Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine learning and CFD communities by deconstructing industrial-scale CFD simulations into their core components. [arXiv](https://arxiv.org/abs/2511.20455) OCT 17, 2025 #### AB-UPT for Automotive and Aerospace Applications In this technical report, we add two new datasets to the body of empirically evaluated use-cases of AB-UPT, combining high-quality data generation with state-of-the-art neural surrogates. [arXiv](https://www.arxiv.org/abs/2510.15808) | [Github](https://github.com/ml-jku/UPT/) OCT 8, 2025 #### GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is understanding plasma turbulence, which significantly impairs plasma confinement, and is vital for next-generation reactor design. [arXiv](https://arxiv.org/abs/2510.07314) | [Github](https://github.com/ml-jku/neural-gyrokinetics) FEB 23, 2025 #### AB-UPT Anchored-Branched Universal Physics Transformer (AB-UPT) for aerodynamics CFD. Handles raw geometry without remeshing at 9M surface and 140M volume cells on a single GPU. [arXiv](https://arxiv.org/abs/2502.09692) | [Github](https://github.com/Emmi-AI/anchored-branched-universal-physics-transformers) NOV 14, 2024 #### NeuralDEM First end-to-end deep learning surrogate for large-scale multi-physics processes. Enables real-time simulation of industrial processes like fluidised bed reactors. [arXiv](https://arxiv.org/abs/2411.09678) | [Github](https://emmi-ai.github.io/NeuralDEM/) FEB 19, 2024 #### UPT: Universal Physics Transformer A Framework For Efficiently Scaling Neural Operators across diverse spatio-temporal problems. Supports both grid and particle simulations. [arXiv](https://arxiv.org/abs/2402.12365) | [Github](https://github.com/ml-jku/UPT)