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Found 6 papers, 4 papers with code
A Multifidelity deep operator network approach to closure for multiscale systems
Projection-based reduced order models (PROMs) have shown promise in representing the behavior of multiscale systems using a small set of generalized (or latent) variables.
Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling
We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost.
Nonlinear proper orthogonal decomposition for convection-dominated flows
Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space.
Interface learning of multiphysics and multiscale systems
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws.
A forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction
In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models.
Dynamical Systems Fluid Dynamics
A long short-term memory embedding for hybrid uplifted reduced order models
In the first layer, we utilize an intrusive projection approach to model dynamics represented by the largest modes.
Fluid Dynamics Dynamical Systems Computational Physics
