Results 151 to 160 of about 6,454,315 (251)

Toward Pore‐Engineered 3D‐Printed Materials for Sorption Water Harvesting, Interfacial Evaporation, and Radiative Cooling

Advanced Materials, EarlyView.
Water harvesting, radiative cooling, and interfacial solar evaporation are fundamentally governed by coupled heat, mass, and light transport processes. These processes are mediated by pore architecture, including pore size, connectivity, and hierarchical organization.
Dejan J. Trajkovski, Higgins M. Wilson, Abdallah Y. M. Ali, Jiaxing Xu, Ruzhu Wang, Primož Poredoš   +5 more
wiley   +1 more source

Learning variable-order time fractional diffusion equations using Physics-Informed Neural Networks. [PDF]

PLoS One
Ren L, Jin S.
europepmc   +1 more source

Bioinspired Mechanosensitive Organic Artificial Neuron With Programmable Excitatory and Inhibitory Responses via Nonlinear Mechano‐Electrochemical Coupling

Advanced Materials, EarlyView.
Organic artificial neurons couple mechanical deformation and ionic environments through nonlinear mechano‐electrochemical dynamics. Mechanical strain and electrolyte concentration reshape their nonlinear electrical characteristics, programming excitatory or inhibitory spiking responses that mimic mechanosensitive biological neurons and enable ...
Rassen Boukraa, Pietro Belleri, Zsolt M. Kovács‐Vajna, Paschalis Gkoupidenis, Fabrizio Torricelli   +4 more
wiley   +1 more source

Forecasting secular variation using physics-informed neural networks for IGRF-14. [PDF]

Earth Planets Space
Shakespeare-Rees N, Livermore PW, Davies CJ, Rogers HF, Beggan CD, Brown WJ, Finlay CC.   +6 more
europepmc   +1 more source

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source

Physics-Informed Neural Networks for Predicting Hydration Degree in Cementitious Systems Blended with Slag and Fly Ash. [PDF]

Materials (Basel)
Hu X, Liao X, Liao Z, Wang Z, Gan M.
europepmc   +1 more source

Percolation‐Driven β‐Relaxation Enables Resonant Acceleration of Crystallization in Amorphous Phase‐Change Materials

Advanced Materials, EarlyView.
Mobile‐atom percolation underlying β‐relaxations fundamentally dictates the crystallization pathways of phase‐change materials. High‐frequency ultrasonic excitation dynamically modulates this critical mobility percolation network, substantially accelerating the crystallization process.
Yu‐Yao Liu, Liang Gao, Jun‐Ying Jiang, Yiming Zhou, Jan Luebben, Di Zhao, Xiaoling Lu, Maximilian J. Müller, Ulrich Boettger, Jiang‐Jing Wang, Hai‐Bin Yu, Shuai Wei   +11 more
wiley   +1 more source

Physics-informed neural networks for predicting laser-tissue interaction in maxillofacial reconstruction surgery. [PDF]

Sci Rep
Yahia ME, Abdikian A, Abdolrasuli H.
europepmc   +1 more source

Advances in Bio‐Integrated Edge Computing Systems for Smart and Connected Health

Advanced Materials, EarlyView.
This review explores advances in bio‐integrated edge computing for healthcare, focusing on co‐designing computing, sensing, and energy strategies to overcome integration and power challenges for optimized, decentralized operation. ABSTRACT Recent advances in bio‐integrated healthcare technologies have enabled continuous, high‐resolution monitoring and ...
Gyuwon Kim, Seungwon Yoo, Tae Woog Kang, Yunuo Huang, Woon‐Hong Yeo   +4 more
wiley   +1 more source

Implementing physics-informed neural networks with deep learning for differential equations. [PDF]

Front Artif Intell
Emmert-Streib F, Tripathi S, Farea A, Holzinger A.   +3 more
europepmc   +1 more source