Results 211 to 220 of about 55,229 (315)
Maximal Dissipation and Well-Posedness of the Euler System of Gas Dynamics. [PDF]
Feireisl E +2 more
europepmc +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
Exponentially (s, P)-Convex Functions and Related Integral Inequalities [PDF]
Nemanja Vučićević
doaj +1 more source
Analyzing the Effectiveness of the Push-Up Method for Idiopathic Scoliosis: A Preliminary Report. [PDF]
Kuroki H +4 more
europepmc +1 more source
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
From planning to execution: Interactive virtual-reality assisted craniotomy planning in meningioma surgery. [PDF]
Lehmann S +6 more
europepmc +1 more source
The Interoperability Challenge in DFT Workflows Across Implementations
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen +13 more
wiley +1 more source
<i>"Solid Hollows"</i> and "<i>Reverspectives"</i>: Similarities and differences. [PDF]
Rogers B, Hughes P.
europepmc +1 more source
Convex bodies and convexity on Grassmann cones
Ewald, G., Shephard, G.C., Busemann, H.
openaire +1 more source
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source

