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Found 4 papers, 0 papers with code
MC-Nonlocal-PINNs: handling nonlocal operators in PINNs via Monte Carlo sampling
We propose, Monte Carlo Nonlocal physics-informed neural networks (MC-Nonlocal-PINNs), which is a generalization of MC-fPINNs in \cite{guo2022monte}, for solving general nonlocal models such as integral equations and nonlocal PDEs.
Gradient-enhanced deep neural network approximations
We propose in this work the gradient-enhanced deep neural networks (DNNs) approach for function approximations and uncertainty quantification.
Solving time dependent Fokker-Planck equations via temporal normalizing flow
In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations.
Robust Sparse Coding via Self-Paced Learning
Sparse coding (SC) is attracting more and more attention due to its comprehensive theoretical studies and its excellent performance in many signal processing applications.
