Results 221 to 230 of about 220,579 (315)

Machine‐Learning‐Based, Feature‐Rich Prediction of Alumina Microstructure from Hardness

Advanced Intelligent Discovery, EarlyView.
Herein, high‐performance generative adversarial network (GAN), named ‘Microstructure‐GAN’, is demonstrated. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed at the nanometer scale in the predicted 1000 ...
Xiao Geng, Jianan Tang, Ningxuan Wen, Jason Shaw, Siddhartha Sarkar, Lionel Vargas‐Gonzalez, Nicholas Ku, Jianhua Tong, Rajendra K. Bordia, Dongsheng Li, Fei Peng   +10 more
wiley   +1 more source

Continuous-Time Quantum Walk in Glued Trees: Localized State-Mediated Almost Perfect Quantum-State Transfer. [PDF]

Entropy (Basel)
Pouthier V, Pepe L, Yalouz S.
europepmc   +1 more source

Unraveling Multimodal Brain Signatures: Deciphering Transdiagnostic Dimensions of Psychopathology in Adolescents

Advanced Intelligent Systems, EarlyView.
This study, utilizing two large‐cohort datasets, employs interpretable neural networks. It demonstrates that incorporating brain morphology and functional and structural networks enhances predictive accuracy for general psychopathology and its dimensions.
Jing Xia, Nanguang Chen, Anqi Qiu
wiley   +1 more source

random walks in random environment and branching random walks

, 2009
This thesis deals with two models of random walks. The first model belongs to the family of random walks in random environment. In the case where the graph is a Galton-watson tree, we are interested in the asymptotic properties of the walk. When the walk is transient, we look at its speed.
openaire   +2 more sources

Eco-evolutionary dynamics of adapting pathogens and host immunity. [PDF]

Elife
Barrat-Charlaix P, Neher RA.
europepmc   +1 more source

Harnessing Deep Learning of Point Clouds for Morphology Mimicking of Universal 3D Shape‐Morphing Devices

Advanced Intelligent Systems, EarlyView.
Soft robots capable of morphing into various 3D shapes are crucial for applications like human‐machine interfaces and biological manipulation. However, controlling 3D shape‐morphing robots with soft actuators remains a challenge. This work introduces a machine learning model that maps complex 3D deformations to control inputs, enabling robots to mimic ...
Jue Wang, Dhirodaatto Sarkar, Jiaqi Suo, Alex Chortos   +3 more
wiley   +1 more source

Adaptive Tip Selection for DAG-Shard-Based Federated Learning with High Concurrency and Fairness. [PDF]

Sensors (Basel)
Xiao R, Cao Y, Xia B.
europepmc   +1 more source

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Advanced Intelligent Systems, EarlyView.
This work introduces random‐forest‐based interpretable generative inverse design (RIGID), a new single‐shot inverse design method for metamaterials using interpretable machine learning and Markov chain Monte Carlo sampling. Once trained on a small dataset, RIGID can estimate the likelihood of designs achieving target behaviors (e.g., wave‐based ...
Wei (Wayne) Chen, Rachel Sun, Doksoo Lee, Carlos M. Portela, Wei Chen   +4 more
wiley   +1 more source

Flowtigs: Safety in flow decompositions for assembly graphs. [PDF]

iScience
Sena F, Ingervo E, Khan S, Prjibelski A, Schmidt S, Tomescu A.   +5 more
europepmc   +1 more source

Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces

Advanced Intelligent Systems, EarlyView.
This review summarizes machine learning (ML)‐assisted simulations and predictions at battery interfaces. It highlights how employing ML algorithms with machine vision, enables the lithium dendrite growth simulation, the solid–electrolyte interphase formation, and other interfacial dynamics.
Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng   +4 more
wiley   +1 more source