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Found 5 papers, 3 papers with code
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs).
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
To address these limitations, we propose the Monte Carlo Neural PDE Solver (MCNP Solver) for training unsupervised neural solvers via the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles.
LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE).
Learning Physics-Informed Neural Networks without Stacked Back-propagation
In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks.
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations
While adopting complex GNN models with more informative message passing and aggregation mechanisms can obviously benefit heterogeneous vertex representations and cooperative policy learning, it could, on the other hand, increase the training difficulty of MARL and demand more intense and direct reward signals compared to the original global reward.
