疎なセンサーデータを最大限に活用するAIの新展開(New twist on AI makes the most of sparse sensor data)

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2023-11-14 ロスアラモス国立研究所(LANL)

◆新しいAIアプローチ「Senseiver」は、エッジコンピューティングを利用して小さな数のセンサーから広範なデータを再構築する。GoogleのPerceiver IOモデルを基にし、ChatGPTの手法を組み合わせ、自然言語処理の技術を適用。
◆この効率的なモデルは、ドローンやセンサーネットワークなどのフィールド展開に適しており、ロスアラモス国立研究所では孤立した油井の特定や特性評価などに利用する計画。他にも自動車、医療モニタリング、クラウドゲーミングなどの実用的な応用に向けた可能性がある。

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疎な観測データからの効率的なフィールド再構築のためのSenseiverの開発 Development of the Senseiver for efficient field reconstruction from sparse observations

Javier E. Santos,Zachary R. Fox,Arvind Mohan,Daniel O’Malley,Hari Viswanathan & Nicholas Lubbers
Nature Machine Intelligence  Published:06 November 2023
DOI:https://doi.org/10.1038/s42256-023-00746-x

疎なセンサーデータを最大限に活用するAIの新展開(New twist on AI makes the most of sparse sensor data)

Abstract

The reconstruction of complex time-evolving fields from sensor observations is a grand challenge. Frequently, sensors have extremely sparse coverage and low-resource computing capacity for measuring highly nonlinear phenomena. While numerical simulations can model some of these phenomena using partial differential equations, the reconstruction problem is ill-posed. Data-driven-strategies provide crucial disambiguation, but these suffer in cases with small amounts of data, and struggle to handle large domains. Here we present the Senseiver, an attention-based framework that excels in reconstructing complex spatial fields from few observations with low overhead. The Senseiver reconstructs n-dimensional fields by encoding arbitrarily sized sparse sets of inputs into a latent space using cross-attention, producing uniform-sized outputs regardless of the number of observations. This allows efficient inference by decoding only a sparse set of output observations, while a dense set of observations is needed to train. This framework enables training of data with complex boundary conditions and extremely large fine-scale simulations. We build on the Perceiver IO by enabling training models with fewer parameters, which facilitates field deployment, and a training framework that allows a flexible number of sensors as input, which is critical for real-world applications. We show that the Senseiver advances the state-of-the-art of field reconstruction in many applications.

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