Results 141 to 150 of about 293,764 (287)

Existence of infinitely many periodic solutions for second-order nonautonomous Hamiltonian systems

open access: yesElectronic Journal of Differential Equations, 2015
By using minimax methods and critical point theory, we obtain infinitely many periodic solutions for a second-order nonautonomous Hamiltonian systems, when the gradient of potential energy does not exceed linear growth.
Wen Guan, Da-Bin Wang
doaj  

“It Is Much Safer to Be Sparse than Connected”: Safe Control of Robotic Swarm Density Dynamics with PDE Optimization with State Constraints

open access: yesAdvanced Intelligent Systems, EarlyView.
This paper proposes a novel control framework to ensure safety of a robotic swarm. A feedback optimization controller is capable of driving the swarm toward a target density while keeping risk‐zone exposure below a safety threshold. Theory and experiments show how safety is more effectively achieved for sparsely connected swarms.
Longchen Niu, Gennaro Notomista
wiley   +1 more source

Predicting Crystal Structures and Ionic Conductivities in Li3 YCl6−x Brx Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential

open access: yesAdvanced Intelligent Systems, EarlyView.
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley   +1 more source

Robust stabilization of LTI negative imaginary systems using the nearest negative imaginary controller

open access: yesIET Control Theory & Applications
This paper considers the problem of robust stabilization of linear time‐invariant systems with respect to unmodelled dynamics and structure uncertainties.
Mohamed Mabrok, Mahmoud Abdelrahim
doaj   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

open access: yesAdvanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez   +4 more
wiley   +1 more source

A System of n = 3 Coupled Oscillators with Magnetic Terms: Symmetries and Integrals of Motion

open access: yesSymmetry, Integrability and Geometry: Methods and Applications, 2005
The properties of a system of n = 3 coupled oscillators with linear terms in the velocities (magnetic terms) depending in two parameters are studied. We proved the existence of a bi-Hamiltonian structure arising from a non-symplectic symmetry, as well ...
Manuel F. Rañada
doaj  

Emergent Spin Hall Quantization and High‐Order van Hove singularities in Square‐Octagonal MA2Z4

open access: yesAdvanced Physics Research, EarlyView.
Square‐octagonal MA2Z4 (M = Mo/W, A = Si/Ge, Z = pnictogen) monolayers are predicted to realize quantum spin Hall insulators with nearly quantized spin Hall conductivity enabled by an emergent spin U(1) quasi‐symmetry. Materials with Z = As and Sb host quasi‐flat bands with high‐order van Hove singularities near the Fermi level, making them promising ...
Rahul Verma   +3 more
wiley   +1 more source

The Progress of Orbitronics: The Enhancement of Orbital Torque Efficiency

open access: yesAdvanced Physics Research, EarlyView.
Orbit‐torque (OT) devices attract significant attention for their low‐power consumption and high stability in applications. This review systematically outlines strategies for enhancing OT efficiency. We categorize approaches into boosting orbital currents/torques and improving the orbital‐to‐spin conversion coefficient.
Pengfei Liu   +6 more
wiley   +1 more source

A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation

open access: yesAdvanced Physics Research, EarlyView.
A hybrid semi‐inverse variational and machine‐learning framework is presented for solving the Schrödinger equation with complex quantum potentials. Physics‐based variational solutions generate high‐quality training data, enabling Random Forest and Neural Network models to deliver near‐perfect energy predictions.
Khalid Reggab   +5 more
wiley   +1 more source

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