A reduced-order physical model describes re-ignition dynamics between consecutive discharges at the same position in dielectric barrier discharge arrangements, enabling time prediction without machine learning.
Uses patch-based autoencoder and neural ODE to build a distributed reduced-order model for studying relative periodic orbits in 2D Kolmogorov turbulence.
NewSee LICENSE fileKolmogorov turbulenceperiodic orbitreduced-order model
Proposes a ladder of information limits to quantify predictability boundaries for reduced-order models under limited information, covering memoryless and memory-augmented trajectory forecasts, event prediction, stationary statistics, and generative laws.
See LICENSE filereduced-order modelinformation theoryprediction theory
Introduces mollified-sharp decomposition for probabilistic regularization of moving shocks in parametric reduced-order models, enhancing shock-capturing capabilities in CFD applications.
See LICENSE fileshock capturereduced-order modelprobabilistic regularization
Proposes a nonlinear reduced-order model with convolutional operator compression to overcome the Kolmogorov N-width limitation of linear projection-based ROMs, achieving accurate approximations with fewer modes for parametrized PDEs.
See LICENSE filereduced-order modelconvolutional operatorN-width
