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Latent Neural PDE Solver for Time-dependent Systems Different from many existing neural network surrogates operating on the high-dimensional discretized field, we propose to learn the dynamics of the system in the latent space with much coarser discretization
GitHub - BaratiLab LNS-Latent-Neural-PDE-Solver In our proposed framework - Latent Neural PDE Solver (LNS), a non-linear autoencoder is first trained to project the full-order representation of the system onto the mesh-reduced space, then a temporal model is trained to predict the future state in this mesh-reduced space
Latent neural PDE solver: : A reduced-order modeling framework for . . . In our proposed framework - Latent Neural PDE Solver (LNS), a non-linear autoencoder is first trained to project the full-order representation of the system onto the mesh-reduced space, then a temporal model is trained to predict the future state in this mesh-reduced space
Latent Neural PDE Solver: a reduced-order modelling framework for . . . Introduction Many intricate physical processes, from the interaction of protein dynamics to the movement of a celestial body, can be described by time-dependent partial diferential equations (PDEs) The simulation of these processes is often conducted by solving these equations numerically,
Latent Neural PDE Solver for Time-dependent Systems TL;DR: We study a framework for efficient reduced-order modelling of time-dependent system Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs)