Results 151 to 160 of about 41,144 (258)

An Uncertainty-Aware Bayesian Deep Learning Method for Automatic Identification and Capacitance Estimation of Compensation Capacitors. [PDF]

Sensors (Basel)
Wang T, Wang P.
europepmc   +1 more source

Credal Bayesian Deep Learning [PDF]

Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of uncertainty are indistinguishable. We present Credal Bayesian Deep Learning (CBDL).
Caprio, Michele; id_orcid 0000-0002-7569-097X, Dutta, Souradeep, Jang, Kuk Jin, Lin, Vivian, Ivanov, Radoslav, Sokolsky, Oleg, Lee, Insup   +6 more
openaire  

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning. [PDF]

Trop Med Infect Dis
Shiddik MAB, Toshi FZ, Yesmin S, Rahman MS.   +3 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Two-Tier heuristic search for ransomware-as-a-service based cyberattack défense analysis using explainable Bayesian deep learning model. [PDF]

Sci Rep
Almuflih AS.
europepmc   +1 more source

Differentiated Impacts of EU Rural Development Measures: A Finite Mixture Evaluation of Italian Olive Farms

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT This paper examines the relationship between participation in the EU Rural Development Program and the economic performance of Italian olive farms using a finite‐mixture model with inverse‐probability‐weighted regression adjustment. Based on 2010–2022 FADN panel data, it estimates heterogeneous treatment effects while correcting for selection ...
Francesco Caracciolo, Marilena Furno
wiley   +1 more source

Inference for Stationary Log-Gaussian Cox Point Processes using Bayesian Deep Learning: Application to Human Oral Microbiome Image Data. [PDF]

Spat Stat
Wang S, Wikle CK, Micheas AC, Welch JLM, Starr JR, Lee KH.   +5 more
europepmc   +1 more source

Import Wheat Tenders and the Effects of the Russian Invasion

Agribusiness, EarlyView.
ABSTRACT Risk and volatility for many commodities escalated sharply following the Russian invasion of Ukraine, creating numerous uncertainties for trading firms and importers. The purpose of this study is to analyze the bidding behavior in Egyptian wheat import tenders in the pre‐ and post‐invasion periods.
William W. Wilson, Sumadhur Shakya, Isaac Dubovoy   +2 more
wiley   +1 more source