Results 81 to 90 of about 13,405 (259)

Advanced Graph–Physics Hybrid Framework (AGPHF) for Holistic Integration of AI-Driven Graph- and Physics- Methodologies to Promote Resilient Wastewater Management in Dynamic Real-World Conditions

Applied Sciences
Wastewater treatment is evolving rapidly with the advent of advanced deep-learning AI, graph-based, and physics-informed approaches. This study integrates graph neural networks, physics-informed neural networks, and multi-agent reinforcement learning ...
Vasileios Alevizos, Nikitas Gerolimos, Zongliang Yue, Sabrina Edralin, Clark Xu, George A. Papakostas, Eleni Vrochidou, George Marnellos, Mousa Mustafa   +8 more
doaj   +1 more source

Structure–Transport–Ion Retention Coupling for Enhanced Nonvolatile Artificial Synapses

Advanced Functional Materials, EarlyView.
Nitrogen incorporation into the conjugated backbone of donor–acceptor polymers enables efficient charge transfer and deep ion embedding in organic electrochemical synaptic transistors (OESTs). This molecular‐level design enhances non‐volatile synaptic properties, providing a new strategy for developing high‐performance and reliable neuromorphic devices.
Donghwa Lee, Jinsung Kim, Sein Chung, Junho Sung, Chaeyeon Han, Eunho Lee   +5 more
wiley   +1 more source

Complex Physics-Informed Neural Network

We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optimizing the activation parameters, compleX-PINN achieves high accuracy with just a single hidden layer.
Si, Chenhao, Yan, Ming, Li, Xin, Xia, Zhihong   +3 more
openaire   +2 more sources

Contact Lens with Moiré Patterns for High‐Precision Eye Tracking

Advanced Functional Materials, EarlyView.
This work presents a passive contact lens for high‐precision eye tracking, integrating a microscopic moiré grating label. The parallax‐induced shift of macroscopic moiré patterns enables angle measurement with 0.28° precision using a standard camera under ambient light.
Ilia M. Fradkin, Roman V. Kirtaev, Mikhail S. Mironov, Dmitriy V. Grudinin, Alexander A. Marchenko, Marina M. Chugunova, Valentin R. Solovei, Alexander V. Syuy, Andrey A. Vyshnevyy, Ilya P. Radko, Aleksey V. Arsenin, Valentyn S. Volkov   +11 more
wiley   +1 more source

Physics‐Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

Advanced Intelligent Systems
This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models
Jingyue Liu, Pablo Borja, Cosimo Della Santina   +2 more
doaj   +1 more source

Ionic‐Electronic Hydrogel‐Liquid Metal Composite Bilayer with Tissue‐Adaptive and Adhesive Properties for Closed‐Loop Neuroprosthetic System

Advanced Functional Materials, EarlyView.
A hydrogel–liquid metal composite peripheral nerve interface (HLB‐PNI) combines electrically durable electrodes and tissue‐adhesive hydrogel for tissue‐adaptive implantation. In nerve‐injured rats, it enables the diagnosis of sensory‐motor connectivity via stimulation and neural signal recording.
Yewon Kim, Kyoungryong Kim, Jaehyon Kim, Heewon Choi, Mikyung Shin, Donghee Son   +5 more
wiley   +1 more source

Evidential Physics-Informed Neural Networks

We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution. The dependent and trainable variables of the PDE residual loss and data-fitting loss terms are recast as ...
Tan, Hai Siong, Wang, Kuancheng, McBeth, Rafe   +2 more
openaire   +2 more sources

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics

Communications Engineering
Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial ...
Weiwei He, Jinzhao Li, Xuan Kong, Lu Deng   +3 more
doaj   +1 more source

Enhancing Synaptic Plasticity and Multistate Retention of Organic Neuromorphic Devices Using Anion‐Excessive Gel Electrolyte

Advanced Functional Materials, EarlyView.
Anion‐excessive gel‐based organic synaptic transistors (AEG‐OSTs) that can maintain electrical neutrality are developed to enhance synaptic plasticity and multistate retention. Key improvement is attributed to the maintenance of electrical neutrality in the electrolyte even after electrochemical doping, which reduces the Coulombic force acting on ...
Yousang Won, Jeongwoo Lee, Hyunwook Kim, Joon Hak Oh   +3 more
wiley   +1 more source