Results 91 to 100 of about 384 (242)
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
Note sur l’intuitionnisme selon Jules Vuillemin
Philosophia ScientiæThe aim of this paper is to take another look at Jules Vuillemin’s interpretation of the notion of intuitionism. We will attempt to show that he develops the idea that a line of reasoning is intuitionistic if it is impossible to find a realistic ...
Théophile Richard
doaj +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
All the mathematics in the world: logical validity and classical set theory
Zagadnienia Filozoficzne w Nauce, 2017 A recognizable topological model construction shows that any consistent principles of classical set theory, including the validity of the law of the excluded third, together with a standard class theory, do not suffice to demonstrate the general validity
David Charles McCarty
doaj
Risk, Technology, and Moral Emotions: Reply to Critics. [PDF]
Sci Eng Ethics, 2020 Roeser S.
europepmc +1 more source
Modern perspectives in Proof Theory. [PDF]
Philos Trans A Math Phys Eng Sci, 2023 Aguilera JP, Pakhomov F, Weiermann A.
europepmc +1 more source
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 +4 more
wiley +1 more source
Unleashing the Constructive Potential of Emotions: Some Critical Comments on Risk, Technology and Moral Emotions by Sabine Roeser. [PDF]
Sci Eng Ethics, 2020 Steinert S.
europepmc +1 more source
The Modal Components of Judgements in a Quantum Model of Psychoanalytic Theory. [PDF]
Entropy (Basel), 2023 Battilotti G, Borozan M, Lauro Grotto R.
europepmc +1 more source
The Interoperability Challenge in DFT Workflows Across Implementations
Advanced Intelligent Discovery, EarlyView.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