Results 61 to 70 of about 47,248 (288)
Sensors, 2022 In order to study the characteristics of pressure fluctuation during unstable combustion, experimental studies had been conducted on the mechanism model of the swirl combustor and the industrial swirl combustor.
Xuhuai Wang +4 more
doaj +1 more source
Multiplicative Dynamic Mode Decomposition
SIAM Journal on Applied Dynamical Systems24 pages, 13 figures.
Nicolas Boullé, Matthew J. Colbrook
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Dynamic mode decomposition for analytic maps
Communications in Nonlinear Science and Numerical Simulation, 2020 14 pages.
Julia Slipantschuk +2 more
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Interpreting the effects of DNA polymerase variants at the structural level
Molecular Oncology, EarlyView.Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi +7 more
wiley +1 more source
Dynamic Mode Decomposition for Compressive System Identification [PDF]
, 2020 Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator. In this work, two recent
Proctor, Joshua L +4 more
core +1 more source
FEBS Open Bio, EarlyView.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
On dynamic mode decomposition: Theory and applications
Journal of Computational Dynamics, 2014 Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, existing DMD theory deals primarily with sequential time series for which the measurement dimension is much larger than the number of measurements taken.
Tu, Jonathan H. +4 more
openaire +3 more sources
Centering Data Improves the Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2020 Dynamic mode decomposition (DMD) is a data-driven method that models high-dimensional time series as a sum of spatiotemporal modes, where the temporal modes are constrained by linear dynamics. For nonlinear dynamical systems exhibiting strongly coherent structures, DMD can be a useful approximation to extract dominant, interpretable modes.
Seth M. Hirsh +3 more
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FEBS Open Bio, EarlyView.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
Modeling the time evolution of the structure factor during polymeric spinodal decomposition using dynamic mode decomposition [PDF]
The development of the microstructure during polymeric spinodal decomposition can be monitored in real time using small-angle scattering. Information about the microstructure can be deduced from measurements of the structure factor—a quantity directly ...
Clarke, N., Jones, M.
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