Real-Time Non-Uniformity Correction Method for 800 FPS High-Frame-Rate Short-Wave Infrared Images. [PDF]
Su G, Wang Y, He D.
europepmc +1 more source
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 +4 more
wiley +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
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
Reducing Complexity in Muscle-Tendon Kinematics Parameterization Improves Convergence Speed in Musculoskeletal Simulations. [PDF]
Harba M, Badia J, Serrancolí G.
europepmc +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Enhancement of airflow uniformity in multi-tray dryers through a CFD-integrated sequential linear programming framework. [PDF]
Asrate DA, Tadle FB, Mihretie YD.
europepmc +1 more source
Sparsity in polynomial optimization
identifier:oai:t2r2.star.titech.ac.jp ...
openaire
Harnessing Machine Learning to Understand and Design Disordered Solids
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
Development of machine learning predictive models for estimating pharmaceutical solubility in supercritical CO<sub>2</sub>: case study on lornoxicam solubility. [PDF]
Chao L +6 more
europepmc +1 more source
An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto +5 more
wiley +1 more source

