Results 121 to 130 of about 646,451 (325)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Fundamental Theorem of Algebra (With Picard's Little Theorem)

, 2012
Jeremy Kun
openalex   +1 more source

Flexible Memory: Progress, Challenges, and Opportunities

Advanced Intelligent Discovery, EarlyView.
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan, Sen Chen, Minghui Guo, Ziliang Cui, Yujie Du, Jing Liu   +5 more
wiley   +1 more source

General Theorem for a Pareto Optimal Plan - Generalization of the First Fundamental Theorem

, 2015
Keiichi Sugiyama
openalex   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

A retraction theorem for topological fundamental groups with application to the Hawaiian earring [PDF]

, 2005
Paul Fabel
openalex   +1 more source

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Advanced Intelligent Discovery, EarlyView.
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun, Tong Wu, Baian Chen, Zikang Li, Qiuyang Lu, Lu Lu, Hon Ho Wong, Cheuk Hei Chan, Bolong Huang   +8 more
wiley   +1 more source

Inmersiones isométricas en variedades riemannianas

Revista Integración, 2011
Este trabajo recapitula la teoría básica de conexiones en fibrados principales y fibrados vectoriales con el fin de aplicar tales teorías al estudio de inmersiones isométricas en variedades riemannianas; por medio de una versión apropiada del teorema ...
Carlos Alberto Marín Arango
doaj  

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

On Conley's fundamental theorem of dynamical systems

International Journal of Mathematics and Mathematical Sciences, 2004
M. R. Razvan
doaj   +1 more source