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arXiv AI

Multi-output LSTM soft sensors for industrial anaerobic co-digestion using LSTM-beta-VAE generative data augmentation

The paper presents a novel approach to monitoring industrial anaerobic co‑digestion by combining a generative LSTM‑beta‑VAE with a multi‑output LSTM soft sensor. By synthesising physically valid sequences of unstable operation from a year of plant data, the method augments sparse datasets and enables simultaneous prediction of key stability indicators such as pH, volatile acid ratio, and biogas production. The augmented model outperforms standard algorithms and unaugmented LSTMs, offering robust early‑warning capabilities for bioprocess stability.

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arXiv Machine Learning
Sep 18

Enhanced Agriculture-informed Neural Network by Domain Knowledge

The paper introduces KAINN, a hybrid neural‑mechanistic model that augments the Agriculture‑informed Neural Network with domain knowledge on fertilizer diffusion, soil respiration, and water‑filled porosity to predict nitrous oxide emissions from agriculture. Experiments across CNN, LSTM, and Transformer architectures show that KAINN achieves lower root mean square error, lower mean absolute error, and higher R-squared values compared to purely data‑driven models and the original AINN. The learned interfaces exhibit smoother, more physically consistent parameter trajectories with reduced uncertainty.

By Ci Lin, Futong Li, Rose Chong-Wu, Tet Yeap, Iluju Kiringa
llmsdiffusionsafety
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arXiv Machine Learning
Oct 15

Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance

The paper introduces a text-informed training data filtering approach that uses large language models to combine unstructured operational text with tabular sensor data for anomaly detection and dataset masking. Applied to a virtual flow meter for oil and gas, the multimodal method reduces mean absolute percentage error from 7.1% to 5.4% compared to sensor-only filtering. Ablation studies confirm that operator comments provide additional filtering signals beyond sensor data alone.

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)
llmsmultimodalbenchmarksdata-engineering
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arXiv AI
Aug 10

Beyond Foundation Models: Dimension-Aware Neural Architecture Search with Small-Data Representation Models for Cryocooler Lifetime Prediction

arXiv:2608. 06993v1 Announce Type: cross Abstract: Large-scale pretrained time-series models achieve strong results through large-scale pretraining and task-agnostic representation learning, but they rely on abundant, diverse data that industrial and scientific domains often lack.

By Gregor Molan (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Grafika Jati (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Francesco Barchi (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Andrea Acquaviva (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Alja\v{z} Osterman (LE-Tehnika d.o.o., \v{S}uceva 27, Kranj, 4000, Slovenia), Martin Molan (Comtrade AI GmbH, Grafenauweg 8, Zug, 6300, Switzerland)
llmssafety
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arXiv Machine Learning
Sep 7

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor

The paper introduces Shallow Recurrent Decoder (SHRED) networks as a data‑driven method for accurate state estimation in engineering systems, specifically applied to the TRIGA Mark II research reactor. SHRED maps sparse sensor measurements to the full state space, handling noisy data and requiring minimal training time. The study demonstrates SHRED’s performance using both synthetic CFD data and experimental temperature recordings, achieving low reconstruction errors and showcasing its potential for real‑time monitoring and digital twin development.

By Stefano Riva, Carolina Introini, Jos\`e Nathan Kutz, Antonio Cammi
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arXiv Machine Learning
Oct 3

Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling

The paper introduces the Variability-Aware Recursive Neural Network (VARNN), a residual-aware architecture for supervised time-series regression that learns a nonlinear, vector-valued residual representation from recent prediction errors. VARNN conditions subsequent predictions on this learned residual-memory state, mapping scalar prediction innovations into a short-context representation. Experiments on nine datasets across energy, healthcare, and environmental domains show that VARNN achieves lower test MSE than static, lag-based, and sequence-model baselines, and ablations confirm that the learned residual memory improves predictive accuracy over direct scalar residual feedback.

By Haroon Gharwi, Yue Dai, Kai Shu
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