Results 141 to 150 of about 3,055 (272)
Modal Interference Drives Madden‐Julian Oscillation Evolution and Predictability
Abstract A data‐driven dynamical filter is developed to characterize Madden‐Julian Oscillation (MJO) variability, by representing tropical variability with nonorthogonal empirical‐dynamical modes that allow for constructive and destructive interference.
David H. Marsico +7 more
wiley +1 more source
Multiplicity of the adjoint representation in simple quotients of the enveloping algebra of a simple Lie algebra [PDF]
Anthony Joseph
openalex +1 more source
Abstract Boundary Delay Systems and Application to Network Flow
ABSTRACT This paper investigates the well‐posedness and positivity of solutions to a class of delayed transport equations on a network. The material flow is delayed at the vertices and along the edges. The problem is reformulated as an abstract boundary delay equation, and well‐posedness is proved by using the Staffans–Weiss theory.
András Bátkai +2 more
wiley +1 more source
ABSTRACT Steering a system towards a desired target in a very short amount of time is a challenging task from a computational standpoint. Indeed, the intrinsically iterative nature of optimal control problems requires multiple simulations of the state of the physical system to be controlled. Moreover, the control action needs to be updated whenever the
Matteo Tomasetto +2 more
wiley +1 more source
One-loop renormalisation of N=1/2 supersymmetric gauge theory in the adjoint representation [PDF]
I. Jack, D.R.T. Jones, L. A. Worthy
openalex +1 more source
ABSTRACT This paper proposes a novel adaptive topology optimization framework that integrates the Virtual Element Method (VEM) with the Material‐Field Series Expansion (MFSE). Within the VEM‐MFSE framework, we propose a material gradient‐driven adaptive strategy, in which elements are refined in regions with higher material density gradients as the ...
Siqi Zhang, Kai Yang, Bing‐Bing Xu
wiley +1 more source
Inverse Design in Nanophotonics via Representation Learning
This review frames machine learning (ML) in nanophotonics through a classification based on where ML is applied. We categorize methods as either output‐side, which create differentiable surrogates for solving Maxwell's partial differential equations (PDEs), or input‐side, which learn compact representations of device geometry.
Reza Marzban +2 more
wiley +1 more source
SRE-FMaps: A Sinkhorn-Regularized Elastic Functional Map Framework for Non-Isometric 3D Shape Matching. [PDF]
Zhang D, Zhang Y, Wang N, Zhao D.
europepmc +1 more source
Topology Optimization of High‐Performance Optomechanical Resonator
Compact mechanical resonators designed to operate at higher‐order modes are presented, achieving high frequencies and enhanced quality factor‐frequency (Qf) products. Using topology optimization, edge losses are reduced and damping is improved. These resonators offer strong performance in a small footprint, making them ideal for quantum transduction ...
Yincheng Shi +4 more
wiley +1 more source
Analysis and optimal control of a fractional order influenza epidemic model. [PDF]
Zahra F, Li Z, Al-Ahmari A.
europepmc +1 more source

