2023-11-14 ロスアラモス国立研究所(LANL)
◆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。
＜関連情報＞
- https://discover.lanl.gov/news/1113-ai-sensor-data/
- https://www.nature.com/articles/s42256-023-00746-x
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
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.
