Results 21 to 30 of about 59 (59)
Accelerating Conjugate Gradient Solvers for Homogenization Problems With Unitary Neural Operators
ABSTRACT Rapid and reliable solvers for parametric partial differential equations (PDEs) are needed in many scientific and engineering disciplines. For example, there is a growing demand for composites and architected materials with heterogeneous microstructures.
Julius Herb, Felix Fritzen
wiley +1 more source
Implicit third‐order Peer two‐step methods that are superconvergent for variable stepsizes have the potential to significantly improve the efficiency of solving large‐scale ODE‐constrained optimal control problems. These include real‐world applications in medical treatment planning for prostate cancer, such as the design of effective three‐dose drug ...
Jens Lang, Bernhard A. Schmitt
wiley +1 more source
Photonic Unitary Circuits for Quantum Information Processing
Unitary transformations are the cornerstone of quantum computing, enabling reversible manipulation of quantum states. This review evaluates photonic waveguide architectures as an evolving solution for quantum computing, exploiting the unique properties of photons. It investigates current theoretical frameworks, material platforms, and design strategies.
Kevin Zelaya +6 more
wiley +1 more source
The Arctic Coastal Erosion Model: Overview, Developments, and Calibration at Drew Point, Alaska
Abstract Permafrost coastlines are experiencing significant erosion as polar amplification has enhanced the effects of climate change in the Arctic. Warmer temperatures are increasing thermo‐denudation and more energetic oceans are increasing thermo‐abrasion in unlithified, ice‐bonded permafrost coastlines which comprise at least 40% of the circum ...
Elyce Bayat +10 more
wiley +1 more source
Efficient Dynamics: Reduced‐Order Modeling of the Time‐Dependent Schrödinger Equation
Reduced‐order modeling (ROM) approaches for the time‐dependent Schrödinger equation are investigated, highlighting their ability to simulate quantum dynamics efficiently. Proper Orthogonal Decomposition, Dynamic Mode Decomposition, and Reduced Basis Methods are compared across canonical systems and extended to higher dimensions.
Kolade M. Owolabi
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
In this paper, we investigate the geometric properties of η‐Ricci–Bourguignon (η‐RB) solitons on para‐Sasakian manifolds equipped with a semisymmetric nonmetric connection (SSNMC). By employing the complete lift on the tangent bundle, we derive curvature relations, Ricci identities, Ricci flow, and the corresponding η‐RB soliton equations for the ...
Lalnunenga Colney +4 more
wiley +1 more source
In this paper, we suggest a rigorous equivalence class description of interval ellipses and near fixed points in coupled metric and G‐metric spaces with applications. In both metric and G‐metric frameworks, we prove that the equivalence classes formed by the division of near fixed points naturally reflect their convergence to underlying fixed points ...
G. Sudhaamsh Mohan Reddy +3 more
wiley +1 more source
Incremental Model Order Reduction of Smoothed‐Particle Hydrodynamic Simulations
The paper presents the development of an incremental singular value decomposition strategy for compressing time‐dependent particle simulation results, addressing gaps in the data matrices caused by temporally inactive particles. The approach reduces memory requirements by about 90%, increases the computational effort by about 10%, and preserves the ...
Eduardo Di Costanzo +3 more
wiley +1 more source
Exploring New Physics Frontiers Through Numerical Relativity. [PDF]
Cardoso V +3 more
europepmc +1 more source

