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Learning-based Multi-continuum Model for Multiscale Flow Problems
Multiscale problems can usually be approximated through numerical homogenization by an equation with some effective parameters that can capture the macroscopic behavior of the original system on the coarse grid to speed up the simulation.
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems
In this work, we propose an adaptive sparse learning algorithm that can be applied to learn the physical processes and obtain a sparse representation of the solution given a large snapshot space.
Iterative Oversampling Technique for Constraint Energy Minimizing Generalized Multiscale Finite Element Method in the Mixed Formulation
The iterative procedure starts with the construction of an energy minimizing snapshot space that can be used for approximating the solution of the model problem.
Numerical Analysis Numerical Analysis
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem
In this work, we propose a multi-agent actor-critic reinforcement learning (RL) algorithm to accelerate the multi-level Monte Carlo Markov Chain (MCMC) sampling algorithms.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
