Results 181 to 190 of about 29,946 (302)
Forecasting secular variation using physics-informed neural networks for IGRF-14. [PDF]
Shakespeare-Rees N +6 more
europepmc +1 more source
Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung +9 more
wiley +1 more source
Implementing physics-informed neural networks with deep learning for differential equations. [PDF]
Emmert-Streib F +3 more
europepmc +1 more source
Analytical solutions are practical tools in ocean engineering, but their derivation is often constrained by the complexities of the real world. This underscores the necessity for alternative approaches.
Deng, J +7 more
core +1 more source
Designable van der Waals Crystal for Artificial Neuronal Cell Mimicking
Designable van der Waals crystal has been demonstrated for device‐scale neuronal cell mimicking. The structural similarity between ion‐channel in biological membranes and layered vdW lattices is realized with nano‐crystallization via Ar + H2S plasma sulfurization.
Jinhyoung Lee +23 more
wiley +1 more source
Peristaltic transport and thermodynamic analysis of hybrid nanofluids in porous media using physics-informed neural networks. [PDF]
Vaseem M, Uddin Z, Upreti H.
europepmc +1 more source
Deep NURBS -- Admissible Physics-informed Neural Networks
In this study, we propose a new numerical scheme for physics-informed neural networks (PINNs) that enables precise and inexpensive solution for partial differential equations (PDEs) in case of arbitrary geometries while strictly enforcing Dirichlet ...
Espath, Luis +2 more
core
We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano +5 more
wiley +1 more source
River Surface Velocity and Discharge Estimation Using Optical Flow and Unlabeled Physics-Informed Neural Networks. [PDF]
Shu Z, Gao Y, Zhang G, Xu Z, Wang J.
europepmc +1 more source
A deep learning inverse‐design framework is established to create versatile reconfigurable terahertz metadevices. By synergizing deep learning with phase‐change materials, this approach enables on‐demand customization of multidimensional electromagnetic responses.
Yisheng Dong +11 more
wiley +1 more source

