Results 71 to 80 of about 287,879 (312)
On Reduced Input-Output Dynamic Mode Decomposition
The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data ...
Benner, Peter +2 more
core +1 more source
Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal ...
Yuya Ohmichi
doaj +1 more source
Estimation of Power System Inertia Using Nonlinear Koopman Modes
We report a new approach to estimating power system inertia directly from time-series data on power system dynamics. The approach is based on the so-called Koopman Mode Decomposition (KMD) of such dynamic data, which is a nonlinear generalization of ...
Hamasaki, Ryo +2 more
core +1 more source
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Data-Driven Pulsatile Blood Flow Physics with Dynamic Mode Decomposition
Dynamic mode decomposition (DMD) is a purely data-driven and equation-free technique for reduced-order modeling of dynamical systems and fluid flow. DMD finds a best fit linear reduced-order model that represents any given spatiotemporal data.
Milad Habibi +2 more
doaj +1 more source
Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians
In this paper we propose a new Koopman operator approach to the decomposition of nonlinear dynamical systems using Koopman Gramians. We introduce the notion of an input-Koopman operator, and show how input-Koopman operators can be used to cast a ...
alvin +11 more
core +1 more source
Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
wiley +1 more source
Elinvar Materials: Recent Progress and Challenges
Elinvar materials, exhibiting temperature‐invariant elastic modulus, are critical for precision instruments and emerging technologies. This article reviews recent progress in the field, with a focus on the anomalous thermoelastic behavior observed in key material systems.
Wenjie Li, Yang Ren
wiley +1 more source
Dynamic Mode Decomposition via Polynomial Root-Finding Methods
Dynamic mode decomposition (DMD) is a powerful data-driven tool for analyzing complex systems that has gained significant attention in various scientific and engineering disciplines.
Gyurhan Nedzhibov
doaj +1 more source

