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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: An Efficient Neural Network for Learning to Solve Parametric Partial Differential Equations without Simulated Data
We investigate the physical information in the MSR loss, which we called long-range entanglements, and identify the challenge that the neural network requires the capacity to model the long-range entanglements in the spatial domain of the PDE, whose patterns vary in different PDEs.
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.
