Search Results for author:
Found 9 papers, 5 papers with code
Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
In this paper, we present a PINN framework for seismic wave inversion in layered (1D) semi-infinite domain.
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data.
Physics-informed Deep Super-resolution for Spatiotemporal Data
High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales.
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena.
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs
Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines.
Encoding physics to learn reaction-diffusion processes
Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs).
Hard Encoding of Physics for Learning Spatiotemporal Dynamics
Modeling nonlinear spatiotemporal dynamical systems has primarily relied on partial differential equations (PDEs).
Physics informed deep learning for computational elastodynamics without labeled data
In this paper, we present a physics-informed neural network (PINN) with mixed-variable output to model elastodynamics problems without resort to labeled data, in which the I/BCs are hardly imposed.
Physics-informed deep learning for incompressible laminar flows
Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inform neural networks, with the need of less data for training a reliable model.
