5 papers
Post-Training Augmentation Invariance
Keenan Eikenberry, Lizuo Liu, Yoonsang Lee
This work develops a framework for post-training augmentation invariance, in which our goal is to add invariance properties to a pretrained network without altering its behavior on…
Parametric Hyperbolic Conservation Laws: A Unified Framework for Conservation, Entropy Stability, and Hyperbolicity
Lizuo Liu, Lu Zhang, Anne Gelb
We propose a parametric hyperbolic conservation law (SymCLaw) for learning hyperbolic systems directly from data while ensuring conservation, entropy stability, and hyperbolicity b…
Non-intrusive structural-preserving sequential data assimilation
Lizuo Liu, Tongtong Li, Anne Gelb
Data assimilation (DA) methods combine model predictions with observational data to improve state estimation in dynamical systems, inspiring their increasingly prominent role in ge…
Entropy stable conservative flux form neural networks
Lizuo Liu, Tongtong Li, Anne Gelb +1
We propose an entropy-stable conservative flux form neural network (CFN) that integrates classical numerical conservation laws into a data-driven framework using the entropy-stable…
Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws
Lizuo Liu, Lu Zhang, Anne Gelb
We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from sol…