Results 71 to 80 of about 47,248 (288)
Data-Driven Pulsatile Blood Flow Physics with Dynamic Mode Decomposition
Fluids, 2020 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
FEBS Open Bio, EarlyView.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
Dynamic Mode Decomposition via Polynomial Root-Finding Methods
MathematicsDynamic 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
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Tensor Dynamic Mode Decomposition
IEEE Signal Processing Letters6 pages, 4 figures, 1 ...
Ziqin He +3 more
openaire +2 more sources
Dynamic Mode Decomposition with Control Liouville Operators
IFAC-PapersOnLine, 2021 This paper builds the theoretical foundations for dynamic mode decomposition (DMD) of control-affine dynamical systems by leveraging the theory of vector-valued reproducing kernel Hilbert spaces (RKHSs). Specifically, control Liouville operators and control occupation kernels are introduced to separate the drift dynamics from the input dynamics.
Joel A. Rosenfeld +1 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.A hierarchical porous copper current collector is fabricated via three‐dimensional printing combined with pressureless sintering to stabilize lithium metal anodes. The interconnected architecture lowers local current density, guides uniform Li deposition within pores, and suppresses dendrite growth.
Alok Kumar Mishra, Mukul Shukla
wiley +1 more source
Surface Tension Measurement of Ti‐6Al‐4V by Falling Droplet Method in Oxygen‐Free Atmosphere
Advanced Engineering Materials, EarlyView.In this article, the temperature‐dependent surface tension of free falling, oscillating Ti‐6Al‐4V droplets is investigated in both argon and monosilane doped, oxygen‐free atmosphere. Droplet temperature and oscillation are captured with one single high‐speed camera, and the surface tension is calculated with Rayleigh's formula.
Johannes May +9 more
wiley +1 more source
Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases
Energies, 2020 Dynamic Mode Decomposition (DMD) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or ...
Binghua Li +3 more
doaj +1 more source
On reduced input-output dynamic mode decomposition [PDF]
Advances in Computational Mathematics, 2018 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 source.
Peter Benner +2 more
openaire +4 more sources