Physics-Based AI-Driven Surrogate Modeling for Structural Displacement Prediction in Mechanical Systems With Limited Sensor Data by Ali Hashemi, Javad Beheshti, Mahdieh Mohammadi

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A data-to-forecast machine learning system for global weather by Xiuyu Sun, Xiaohui Zhong, Xiaoze Xu, Yuanqing Huang, Hao Li, J. David Neelin, Deliang Chen, Jie Feng, Wei Han, Libo Wu, Yuan Qi

“…Recent advances in machine learning present a promising alternative, but still depend on the initial conditions generated by NWP systems. …”
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TEEMLEAP—A New Testbed for Exploring Machine Learning in Atmospheric Prediction for Research and Education by J. Wilhelm, J. Quinting, M. Burba, S. Hollborn, U. Ehret, I. Pena Sánchez, S. Lerch, J. Meyer, B. Verfürth, P. Knippertz

“…Abstract In the past 5 years, data‐driven prediction models and Machine Learning (ML) techniques have revolutionized weather forecasting. …”
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Machine learning-enhanced fully coupled fluid–solid interaction models for proppant dynamics in hydraulic fractures by Dennis Delali Kwesi Wayo, Sonny Irawan, Lei Wang, Leonardo Goliatt

“…Abstract This study presents a hybrid modeling framework for predicting proppant settling rate (PSR) in hydraulic fracturing by integrating symbolic physics-based derivations, parametric simulations, and ensemble machine learning. …”
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Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests by Daniel Camacho-Gomez, Carlos Borau, Jose Manuel Garcia-Aznar, Maria Jose Gomez-Benito, Mark Girolami, Maria Angeles Perez

“…We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based modeling and machine learning in digital twins. …”
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Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses by Hossein Gholizadeh, T. Prabhakar Clement, Christopher T. Green, Geoffrey R. Tick, Alain M. Plattner, Yong Zhang

“…To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

“…This study presents an advanced framework integrating multi-source remote sensing data fusion, physically based modeling, and machine learning to enable high-resolution and high-precision FMC estimation. …”
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An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications by Ahmed Shakir Ali Ali, Farhad Jazaei, Peyman Babakhani, Muhammad Masood Ashiq, Alireza Bakhshaee, Brian Waldron

“…However, interest has increased over the past few years in using Machine Learning (ML) approaches, like Deep Learning (DL) techniques, to study groundwater fluctuation dynamics more efficiently. …”
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Quantum equilibrium propagation for efficient training of quantum systems based on Onsager reciprocity by Clara C. Wanjura, Florian Marquardt

“…Abstract The widespread adoption of machine learning and artificial intelligence in all branches of science and technology creates a need for energy-efficient, alternative hardware. …”
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Mechanisms and modelling of diffusion in solids: a multiscale framework with industrial case studies and AI enhancements by Barah, Obinna Onyebuchi, Natukunda, Faith, Bori, Ige, Ukagwu, Kelechi John

“…The review also explores the emerging role of artificial intelligence (AI) and machine learning (ML) in predicting diffusion coefficients, activation barriers, and optimal processing conditions. …”
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Repurposing drugs for the human dopamine transporter through WHALES descriptors-based virtual screening and bioactivity evaluation by Ding Luo, Zhou Sha, Junli Mao, Jialing Liu, Yue Zhou, Haibo Wu, Weiwei Xue

“…Computational approaches, encompassing both physics-based and machine learning (ML) methodologies, have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities. …”
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