Results 81 to 90 of about 696 (152)

Design for flexibility: An adjustable robust optimization approach with decision‐dependent uncertainty

open access: yesAIChE Journal, EarlyView.
ABSTRACT Flexibility is a crucial characteristic of industrial systems that face increasing volatilities and is therefore essential to ensure feasible operation under uncertainty. Flexibility is often closely tied to the design of a system, and careful consideration must be taken to understand the trade‐off between design cost and operational ...
Jnana Sai Jagana   +3 more
wiley   +1 more source

Real-time warning method for sand plugging in offshore fracturing wells. [PDF]

open access: yesSci Rep
Xu Y   +8 more
europepmc   +1 more source

Designing Memristive Materials for Artificial Dynamic Intelligence

open access: yesAdvanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

open access: yesAdvanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang   +5 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

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

open access: yesAdvanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour   +5 more
wiley   +1 more source

Home - About - Disclaimer - Privacy