Results 181 to 190 of about 8,084,029 (292)
Physics-Informed Neural Network-Based Elevator Degradation Diagnosis and Early Warning. [PDF]
Li R +6 more
europepmc +1 more source
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
A Physics-Informed Neural Network Scheme for Shortcuts to Adiabaticity in Three-Level Non-Hermitian Quantum Systems. [PDF]
Liu M +6 more
europepmc +1 more source
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
Volumetric Ablation of Phenolic Resin in Extreme Environments: A Variable-Property Physics-Informed Neural Network Framework. [PDF]
Du W, Ma T, Song H.
europepmc +1 more source
Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics
“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 +3 more
wiley +1 more source
A dual physics-informed neural network for topology optimization
We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) with the traditional SIMP-based topology optimization (TO) algorithm.
Singh, Ajendra +2 more
core
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
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 +3 more
wiley +1 more source
Recently, Physics-Informed Neural Networks have displayed great potential in delivering swift and accurate solutions for inverse problems. Here, a Physics-Informed Neural Network (PINN) was used to solve the inverse heat conduction problem in the ...
Goutham Krishna (20382381) +2 more
core
Physics-Informed Neural Network Prediction of Methane Hydrate Flash Crystallization and Dissociation in Microfluidic Confinement. [PDF]
Dale S +5 more
europepmc +1 more source

