Results 151 to 160 of about 834,629 (316)

Mapping uncertainty using differentiable programming

open access: yesAIChE Journal, EarlyView.
Abstract Uncertainty quantification (UQ) and propagation is a ubiquitous challenge in science, permeating our field in a general fashion, and its importance cannot be overstated. Recently, the commoditization of differentiable programming, motivated by the development of machine learning, has allowed easier access to tools for evaluating derivatives of
Victor Alves   +3 more
wiley   +1 more source

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

open access: yesAdvanced 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

Flexible Memory: Progress, Challenges, and Opportunities

open access: yesAdvanced 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   +5 more
wiley   +1 more source

Some asymptotic fixed point theorems [PDF]

open access: bronze, 1972
Roger D. Nussbaum
openalex   +1 more source

On fixed point theorem

open access: yesProceedings of the Japan Academy, Series A, Mathematical Sciences, 1974
openaire   +3 more sources

Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions

open access: yesAdvanced Intelligent Systems, EarlyView.
This study introduces a new framework for decision‐making in unmanned combat aerial vehicles (UCAVs), integrating graph convolutional networks and hierarchical reinforcement learning (HRL). The method tackles adopts a curriculum‐based training approach guided by cross‐entropy rewards.
Ruihai Chen   +4 more
wiley   +1 more source

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