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Found 5 papers, 2 papers with code
Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods.
Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
In this paper, we introduce the Crystal Growth Neural Emulator (CGNE), a probabilistic model for efficient crystal growth emulation at the mesoscopic scale that overcomes these challenges.
Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates
State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data.
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
However, to incorporate symmetries in probabilistic neural simulators that can simulate stochastic phenomena, we need a model that produces equivariant distributions over trajectories, rather than equivariant function approximations.
Towards Learned Simulators for Cell Migration
Simulators driven by deep learning are gaining popularity as a tool for efficiently emulating accurate but expensive numerical simulators.
