Results 201 to 210 of about 342,941 (283)

High-Precision Spatial Interpolation of Meteorological Variables in Complex Terrain Using Machine Learning Methods. [PDF]

Sensors (Basel)
Li S, Zhang B, Shi B, Ai Q, Zeng Y, Yan X, Chen H, Wang H.   +7 more
europepmc   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

Downscaling the spatial resolution of satellite imagery based on morphometric parameters to estimate the Topographic Wetness Index using GIS tools. [PDF]

Sci Rep
Shabbir H, Ehsan M, Raza D, Sohail MT, Alqahtani H, Almutairi R, Almutairi Y.   +6 more
europepmc   +1 more source

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions. [PDF]

RSC Adv
Kim SJ, Kang SS, Pae KN, Pak SI, Jo HI, Kim RJ.   +5 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Sensor-Based Ozone Monitoring and Forecasting in a Synchrotron Radiation Laboratory Using Autoregressive Integrated Moving Average Models. [PDF]

Sensors (Basel)
Wen PJ, Wu KW, Ho LC, Yang CH, Tsai TH, Fang SH.   +5 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A Metric-Driven Evaluation Framework for Remaining Useful Life Prognosis with Quantified Uncertainty. [PDF]

Sensors (Basel)
Vashishtha G, Chauhan S, Ertarğın M.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source