Search Results for author:
Found 6 papers, 1 papers with code
Inverse analysis of granular flows using differentiable graph neural network simulator
While machine learning-based surrogate models offer computational efficiency and differentiability, they often struggle to generalize beyond their training data due to their reliance on low-dimensional input-output mappings that fail to capture the complete physics of granular flows.
Three-dimensional granular flow simulation using graph neural network-based learned simulator
Surrogate models based on statistical or machine learning methods are a viable alternative, but they are typically empirical and rely on a confined set of parameters in evaluating associated risks.
Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problems
We propose a novel hybrid GNS/Material Point Method (MPM) to accelerate forward simulations by minimizing error on a pure surrogate model by interleaving MPM in GNS rollouts to satisfy conservation laws and minimize errors achieving 24x speedup compared to pure numerical simulations.
Graph Neural Network-based surrogate model for granular flows
Due to their permutation-dependent learning, traditional machine learning-based models require huge training data to generalize.
A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading
The LSTM model features include the relative density of soil and the previous stress history to predict the pore water pressure response.
Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction
This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data.
