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Found 4 papers, 2 papers with code
TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs
We introduce the Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN) for accomplishing nonlinear model order reduction (MOR) of transport-dominated partial differential equations in an MOR-integrating PINNs framework.
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs
The meta-network adaptively ``learns'' the parametric dependence of the system and ``grows'' this hidden layer one neuron at a time.
Fast $L^2$ optimal mass transport via reduced basis methods for the Monge-Amp$\grave{\rm e}$re equation
Repeatedly solving the parameterized optimal mass transport (pOMT) problem is a frequent task in applications such as image registration and adaptive grid generation.
Reduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm
The goal of these methods is to map the given high-dimensional data into a low-dimensional space preserving certain properties of the initial data.
