# 塑造产业的物理AI研究

- 来源：Mistral AI：News（网页）
- 发布时间：2026-05-27 00:00
- AIHOT 分数：55
- AIHOT 标记：精选
- AIHOT 链接：https://aihot.virxact.com/items/cmppdcr7d0e32slv4rwk67i43
- 原文链接：https://mistral.ai/news/physics-ai-research

## 精选理由

Mistral 把物理 AI 定为下个重点，从流体仿真到核聚变等离子体都有论文支撑。搞工业仿真的团队值得跟进，但这次没有新模型发布，更多是路线宣示而非新突破。

## AI 摘要

Mistral AI通过收购Emmi AI，强化其在推动AI研究前沿与工业工程解决方案方面的投入。其目标是为航空航天、汽车、半导体和能源等塑造物理世界的核心产业构建基础性物理AI模型，以加速工程开发。此项研究基于一系列已发表的突破性成果，包括：用于模拟超音速湍流的3D机翼CFD数据集、计算流体动力学基础模型的前瞻综述、应用于汽车与航空的AB-UPT模型，以及用于聚变等离子体湍流模拟的GyroSwin模型。此前已开源的UPT（通用物理Transformer）和NeuralDEM等成果也为此研究奠定了基础。

## 正文

2 min read

The acquisition of 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

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

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 | Github

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 | Github

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 | Github

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 | Github

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 | Github

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