Nvidia Blog 02月16日
Amphitrite Rides AI Wave to Boost Maritime Shipping, Ocean Cleanup With Real-Time Weather Prediction and Simulation
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法国初创公司Amphitrite利用卫星数据和人工智能,模拟并预测海洋洋流和天气,为海运和海洋垃圾收集作业带来革新。其基于NVIDIA AI和Earth-2平台的人工智能模型,能够帮助船只优化航线,节省燃料,减少碳排放。通过分析洋流,Amphitrite的模型能帮助船只选择最佳航行时间和航线,从而提高效率并降低环境影响。此外,该公司还利用NASA和欧洲航天局的公共数据,结合计算机视觉和AI技术,提供比传统方法更可靠的海洋洋流和天气建模。

🌊Amphitrite是一家法国初创公司,它利用卫星数据和AI技术来模拟和预测海洋洋流和天气,旨在提高海运效率和减少环境污染。

🚢Amphitrite的人工智能模型通过分析洋流,帮助船只优化航线,减少燃料消耗和碳排放,从而支持能源转型和解决环境问题。该模型基于NVIDIA AI和Earth-2平台,能够提供关于船舶定位的最佳方案,以便更好地利用洋流的力量。

🛰️Amphitrite的模型基于NASA和欧洲航天局的公共数据,并结合了计算机视觉和AI技术,从而提供比传统方法更准确的全球洋流分析。他们也是世界上第一个纳入SWOT卫星数据的预测模型,SWOT是由NASA和法国航天局CNES共同开发的卫星项目。

👩‍💻Amphitrite的AI团队由计算机视觉科学家Hannah Bull领导,团队成员以女性为主,这在行业内较为罕见。公司以希腊神话中的海之女神Amphitrite命名,以纪念这位强大但常被忽视的女性形象。

Named after Greek mythology’s goddess of the sea, France-based startup Amphitrite is fusing satellite data and AI to simulate and predict oceanic currents and weather.

It’s work that’s making waves in maritime-shipping and oceanic litter-collection operations.

Amphitrite’s AI models — powered by the NVIDIA AI and Earth-2 platforms — provide insights on positioning vessels to best harness the power of ocean currents, helping ships know when best to travel, as well as the optimal course. This helps users reduce travel times, fuel consumption and, ultimately, carbon emissions.

“We’re at a turning point on the modernization of oceanic atmospheric forecasting,” said Alexandre Stegner, cofounder and CEO of Amphitrite. “There’s a wide portfolio of applications that can use these domain-specific oceanographic AI models — first and foremost, we’re using them to help foster the energy transition and alleviate environmental issues.”

Optimizing Routes Based on Currents and Weather

Founded by expert oceanographers, Amphitrite — a member of the NVIDIA Inception program for cutting-edge startups — distinguishes itself from other weather modeling companies with its domain-specific expertise.

Amphitrite’s fine-tuned, three-kilometer-scale AI models focus on analyzing one parameter at a time, making them more accurate than global numerical modeling methods for the variable of interest. Read more in this paper showcasing the AI method, dubbed ORCAst, trained on NVIDIA GPUs.

Depending on the user’s needs, such variables include the current of the ocean within the first 10 meters of the surface — critical in helping ships optimize their travel and minimize fuel consumption — as well as the impacts of extreme waves and wind.

“It’s only with NVIDIA accelerated computing that we can achieve optimal performance and parallelization when analyzing data on the whole ocean,” said Evangelos Moschos, cofounder and chief technology officer of Amphitrite.

Using the latest NVIDIA AI technologies to predict ocean currents and weather in detail, ships can ride or avoid waves, optimize routes and enhance safety while saving energy and fuel.

“The amount of public satellite data that’s available is still much larger than the number of ways people are using this information,” Moschos said. “Fusing AI and satellite imagery, Amphitrite can improve the accuracy of global ocean current analyses by up to 2x compared with traditional methods.”

Fine-Tuned to Handle Oceans of Data

The startup’s AI models, tuned to handle seas of data on the ocean, are based on public data from NASA and the European Space Agency — including its Sentinel-3 satellite.

Plus, Amphitrite offers the world’s first forecast model incorporating data from the Surface Water and Ocean Topography (SWOT) mission — a satellite jointly developed and operated by NASA and French space agency CNES, in collaboration with the Canadian Space Agency and UK Space Agency.

“SWOT provides an unprecedented resolution of the ocean surface,” Moschos said.

While weather forecasting technologies have traditionally relied on numerical modeling and computational fluid dynamics, these approaches are harder to apply to the ocean, Moschos explained. This is because oceanic currents often deal with nonlinear physics. There’s also simply less observational data available on the ocean than on atmospheric weather.

Computer vision and AI, working with real-time satellite data, offer higher reliability for oceanic current and weather modeling than traditional methods.

Amphitrite trains and runs its AI models using NVIDIA H100 GPUs on premises and in the cloud — and is building on the FourCastNet model, part of Earth-2, to develop its computer vision models for wave prediction.

According to a case study along the Mediterranean Sea, the NVIDIA-powered Amphitrite fine-scale routing solution helped reduce one shipping line’s carbon emissions by 10%.

Through NVIDIA Inception, Amphitrite gained technical support when building its on-premises infrastructure, free cloud credits for NVIDIA GPU instances on Amazon Web Services, as well as opportunities to collaborate with NVIDIA experts on using the latest simulation technologies, like Earth-2 and FourCastNet.

Customers Set Sail With Amphitrite’s Models

Enterprises and organizations across the globe are using Amphitrite’s AI models to optimize their operations and make them more sustainable.

CMA-CGM, Genavir, Louis Dreyfus Armateurs and Orange Marine are among the shipping and oceanographic companies analyzing currents using the startup’s solutions.

In addition, Amphitrite is working with a nongovernmental organization to help track and remove pollution in the Pacific Ocean. The initiative uses Amphitrite’s models to analyze currents and follow plastics that drift from a garbage patch off the coast of California.

Moschos noted that another way the startup sets itself apart is by having an AI team — led by computer vision scientist Hannah Bull — that comprises majority women, some of whom are featured in the image above.

“This is still rare in the industry, but it’s something we’re really proud of on the technical front, especially since we founded the company in honor of Amphitrite, a powerful but often overlooked female figure in history,” Moschos said.

Learn more about NVIDIA Earth-2.

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Amphitrite 海洋洋流 人工智能 卫星数据 NVIDIA
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