Results 151 to 160 of about 6,454,505 (250)

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

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

Physics-Informed Neural Networks with Hard Linear Equality Constraints

Surrogate modeling is used to replace computationally expensive simulations. Neural networks have been widely applied as surrogate models that enable efficient evaluations over complex physical systems.
Flores, Gonzalo E. Constante, Chen, Hao, Li, Can   +2 more
core  

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 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

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 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

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 laser-tissue interaction in maxillofacial reconstruction surgery. [PDF]

Sci Rep
Yahia ME, Abdikian A, Abdolrasuli H.
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

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