Results 101 to 110 of about 6,454,315 (251)

Physics-Informed Neural Networks for Heat Pump Load Prediction

Energies
Heat pumps are promising solutions for managing the increasing heating demand of residential houses, reducing the environmental impact when used with renewable energy.
Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Ionut Anghel, Andrei Pelle   +4 more
doaj   +1 more source

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

Unraveling particle dark matter with Physics-Informed Neural Networks

Physics Letters B
We parametrically solve the Boltzmann equations (BEs) governing freeze-in dark matter (DM) in alternative cosmologies with Physics-Informed Neural Networks (PINNs), a mesh-free method.
M.P. Bento, H.B. Câmara, J.F. Seabra
doaj   +1 more source

Bio‐Inspired Artificial Ionic Mechanoreceptor

Advanced Functional Materials, EarlyView.
A skin‐inspired artificial mechanoreceptor based on ionic interactions is presented for biomimetic tactile sensing. Pressure‐driven ionic redistribution within microfluidic channels generates a self‐powered electrical signal without external bias. The generated waveform exhibits mechanoreceptor‐like temporal features, including overshoot and undershoot,
Mohammad Akbari, Jeppe Don, Lasse A. N. Pedersen, John Noee, Rassoul Tabassian   +4 more
wiley   +1 more source

M‐ENIAC: A Physics‐Informed Machine Learning Recreation of the First Successful Numerical Weather Forecasts

Geophysical Research Letters
In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here,
Rüdiger Brecht, Alex Bihlo
doaj   +1 more source

Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks

Universe
We examine the use of a novel variant of Physics-Informed Neural Networks to predict cosmological parameters from recent supernovae and baryon acoustic oscillations (BAO) datasets. Our machine learning framework generates uncertainty estimates for target
Hai Siong Tan
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

Application of Physics informed Neural Networks

U ovom radu su istražene fizikom informirane neuronske mreže (engl. Physics-informed Neural Networks, PINN). Objašnjene su tehnike strojnog učenja i neuronskih mreža, kao i metode fizikalnog modeliranja i rješavanja diferencijalnih jednadžbi.
Maretić, Mislav
core  

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Applied Physics-Informed Neural Networks for Spacecraft Magnetic Testing

Aerospace
Artificial intelligence and machine learning techniques can be used for performing magnetic testing on spacecraft that has historically been difficult and risky to perform.
Andrew Mentges, Bharat Rawal
doaj   +1 more source