Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by poor-quality training data, which obscures underlying physical laws and degrades predictive accuracy.
By Kristoffer Hjelmen Nesland (University of Stavanger, Stavanger, Norway, Solution Seeker AS, Oslo, Norway), Bjarne Grimstad (Solution Seeker AS, Oslo, Norway), Antorweep Chakravorty (University of Stavanger, Stavanger, Norway), Dan Sui (University of Stavanger, Stavanger, Norway)arXiv - stat.ML
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Originally published by arXiv - stat.ML on Oct 15, 2026.
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