Results 71 to 80 of about 163,902 (301)

Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

Advanced Energy Materials, EarlyView.
This perspective highlights how machine learning accelerates sustainable energy materials discovery by integrating quantum‐accurate interatomic potentials with property prediction frameworks. The evolution from statistical methods to physics‐informed neural networks is examined, showcasing applications across batteries, catalysts, and photovoltaics ...
Kwang S. Kim
wiley   +1 more source

On Exceptional Sets of the Hilbert Transform [PDF]

Real Analysis Exchange, 2017
We prove several theorems concerning the exceptional sets of Hilbert transform on the real line. In particular, it is proved that any null set is exceptional set for the Hibert transform of an indicator function. The paper also provides a real variable approach to the Kahane-Katsnelson theorem on divergence of Fourier series.
openaire   +4 more sources

Toward efficient quantum computation of molecular ground‐state energies

AIChE Journal, EarlyView.
Abstract Variational quantum eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classical computer to optimize the parameters of a trial wave function, while the quantum computer simulates the energy by preparing and measuring a set of bitstring ...
Farshud Sorourifar, Mohamed Taha Rouabah, Nacer Eddine Belaloui, Mohamed Messaoud Louamri, Diana Chamaki, Erik J. Gustafson, Norm M. Tubman, Joel A. Paulson, David E. Bernal Neira   +8 more
wiley   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Bilinear Transformations in Hilbert Space [PDF]

Transactions of the American Mathematical Society, 1939
Introduction. A function of two variables h = F(f, g), where h, f, and g are all elements of Hilbert Space may be termed a bilinear transformation if it is linear in f and linear in g. A more formal definition is given in ?1. While a complete treatment of bilinear transformations would obviously require a very lengthy discussion, we wish to point out ...
openaire   +2 more sources

The Adaptive Trajectory of the Normal Force Vector in the Polishing of Curved Surface Component Robots

Advanced Intelligent Systems, EarlyView.
This study uses iterative learning control and voice coil motor to keep normal force constant in curved surface polishing. A mechanism‐data fusion model adjusts robotic posture via real‐time feedback for adaptive tracking control of normal force vector direction.
Jiale Xu, Haibin Huang, Hui Long, Shijing Lei   +3 more
wiley   +1 more source

Classification and Identification of Underwater Target based on Sound Propagation

نشریه مهندسی دریا, 2018
This paper investigates an underwater noise target classification algorithm in order to identify vessels in shallow water. To this aim the Hilbert Huang transform has been used to extract features in order to be used in a classifier.
Hassan Sayyaadi, Mohammad Saeed Seif, Aliasghar Abniki   +2 more
doaj  

On Solving Modified Time Caputo Fractional Kawahara Equations in the Framework of Hilbert Algebras Using the Laplace Residual Power Series Method

Fractal and Fractional
In this work, we first develop the modified time Caputo fractional Kawahara Equations (MTCFKEs) in the usual Hilbert spaces and extend them to analogous structures within the theory of Hilbert algebras.
Faten H. Damag, Amin Saif
doaj   +1 more source

Enhanced Embolic Signal Analysis through Ensemble Deep Learning Techniques Utilizing Transfer Learning and Layer Freezing Strategies

Advanced Intelligent Systems, EarlyView.
A 10‐convolutional neural network ensemble using transfer learning and layer freezing analyzes 400 Doppler‐ultrasound spectrograms to distinguish speckle, artifact, and ESs. Soft and hard voting drive performance to 96.7% accuracy and 96.5% F1, highlighting a practical route for early embolus detection and stroke‐risk mitigation.
M. Ikbal Karadeli, Berke Cansiz, Nizamettin Aydin, Gorkem Serbes   +3 more
wiley   +1 more source