arXiv Machine Learning

An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis

arXiv Machine Learning

Aug 18

Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks

arXiv:2608. 14764v1 Announce Type: new Abstract: With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical.

By Bego\~na Ispizua, Serio Gil-L\'opez, Leire Arrizabalaga, Ibai La\~na

arXiv Machine Learning

Sep 22

Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation

arXiv:2609.22843v1 Announce Type: cross Abstract: Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applicat...

By Amir Madmolilvand, Farzaneh Abdollahi

safety

arXiv AI

Aug 18

Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity

arXiv:2608. 16612v1 Announce Type: cross Abstract: An accurate estimation of the state of health (SOH) underpins a safe and optimized use of the battery system.

By Jiaqi Yao, Julia Kowal

fine-tuning efficiency

arXiv Machine Learning

Aug 3

PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction

arXiv:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.

By Zeping Chen, Ruda Jian, Sachin Sigdel, Guoping Xiong, Jian-Xun Wang, Tengfei Luo

Hugging Face Trending Papers

Sep 19

Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation

Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applications such as electric vehicles and energy storage s...

safety

arXiv Machine Learning

Jul 24

AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries

arXiv:2607. 20577v1 Announce Type: new Abstract: Physics-based simulations are essential for understanding the electrode-scale discharge behavior of lithium-ion batteries (LIBs) but suffer from prohibitive computational costs.

By Mengda Xing (CRIL, UA), Jean-Marie Lagniez (CRIL, UA), Alejandro Franco (LRCS)

llms efficiency benchmarks

arXiv Machine Learning

Jun 29

Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte

arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.

By Gift Modekwe, Qiugang Lu

diffusion fine-tuning

arXiv Machine Learning

Sep 21

Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data

arXiv:2609.21932v1 Announce Type: new Abstract: Joint remaining useful life (RUL) prediction and capacity estimation require representations of both gradual degradation and recent battery behavior. T...

By Khoa Tran, Ho-Si-Hung Nguyen, Phone Wai Yan Moe, Hung-Cuong Trinh, Thi-Hoang-Giang Tran

llms

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

llms diffusion safety

arXiv AI

Jun 11

Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation

arXiv:2606. 11990v1 Announce Type: cross Abstract: Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models.

By Amir El-Ghoussani, Michele De Vita, Ronald Naumann, Valiseios Belagiannis

llms rag

arXiv AI

Jul 20

AquaAugmentor: A Novel Feature Augmentation Algorithm for Water Potability Prediction

arXiv:2607. 15775v1 Announce Type: cross Abstract: Access to potable water is crucial for health, economic development, and sustainability.

By Muntasir Tabasum, Al Zadid Sultan Bin Habib, Tanpia Tasnim, Md. Ekramul Islam, Md Younus Ahamed, Md Asif Bin Syed

arXiv Machine Learning

Sep 10

Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning

The paper presents a hybrid neural architecture that blends linear and nonlinear feed‑forward networks for day‑ahead electricity price forecasting. It introduces a partial online learning strategy with warm‑starting and stage‑specific hyperparameters to cut computational time, and employs Bernstein Online Aggregation to combine forecasts. Experiments on six years of major European markets show the method reduces RMSE by 11‑12% and MAE by 14‑17% compared to state‑of‑the‑art benchmarks while lowering computational cost.

By Btissame El Mahtout, Florian Ziel

benchmarks

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