Results 101 to 110 of about 11,198,121 (288)

Physics-Informed Machine Learning For Sound Field Estimation

CoRR
Accepted to IEEE Signal Processing Magazine, Special Issue on Model-based and Data-Driven Audio Signal ...
Shoichi Koyama, Juliano G. C. Ribeiro, Tomohiko Nakamura, Natsuki Ueno, Mirco Pezzoli   +4 more
openaire   +3 more sources

Multimodal Optoelectronic Synapses for Neuromorphic Intelligence: Coupling Light, Electrical, Thermal, and Beyond

Advanced Functional Materials, EarlyView.
This review revisits optoelectronic neuromorphic devices that extend from unimodal sensing toward multimodal perception. Five types of stimuli‐to‐output information flow that are classified based on the device roles within the system and applications are summarized and discussed.
Jihyun Shin, Junhyung Cho, Wangmyung Choi, Jaehyun Hur, Jae Kyeong Jeong, Ulrike Kraft, Hocheon Yoo   +6 more
wiley   +1 more source

Modeling Nickel-Silicidation Using Physics-Informed Machine Learning

We simulate nickel silicidation in one and two space dimensions via physics-informed machine learning. Our machine learning models are solely trained on the governing physical laws in the form of a reaction-diffusion system, without requiring measurement
Pixius, Christophe, Mundinar, Simon, Straub, Christopher, Klonnek, Jessica, Roßkopf, Andreas   +4 more
core   +1 more source

Physics-informed extreme learning machine (PIELM) for consolidation around an expanded cylindrical cavity.

PLoS ONE
This paper proposes a physics-informed extreme learning machine (PIELM) for analyzing consolidation immediately after cavity expansion. The deep neural networks in traditional physics-informed neural network (PINN) framework are substituted by the ...
Chuan-Qin Pang, Zhu-Hao Zhang, Si-Han Chen, Hong-Ya Yue, Yu Zhang   +4 more
doaj   +1 more source

Physics-informed machine learning for moving load problems

Journal of Physics: Conference Series
Abstract This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to predict the deflection of beams and the magnitude of the loads.
Taniya Kapoor, Hongrui Wang 0001, Alfredo Núñez, Rolf P. B. J. Dollevoet   +3 more
openaire   +3 more sources

Emerging Post‐CMOS Hardware Neurons for Brain‐Inspired Computing: Devices, Circuits, and System Integration

Advanced Functional Materials, EarlyView.
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan, Hocheon Yoo, Benoît H. Lessard, Ioannis Kymissis, Chang‐Hyun Kim   +4 more
wiley   +1 more source

Learning strategies for physics-informed neural networks

Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them. This thesis studies the challenges
Wong, Jian Cheng
core   +1 more source

Implementing physics-informed neural networks with deep learning for differential equations

Frontiers in Artificial Intelligence
Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs).
Frank Emmert-Streib, Frank Emmert-Streib, Shailesh Tripathi, Amer Farea, Andreas Holzinger   +4 more
doaj   +1 more source

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot

Advanced Functional Materials, EarlyView.
This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio, Jonas Elsborg, Elif Coşkun, Andreas Burger, Pradipkumar Manubhai Leuaa, Thu Pham Anh Phan, Jin Hyun Chang, Christodoulos Chatzichristodoulou   +7 more
wiley   +1 more source