Physics-informed neural networks with periodic activation functions for solute transport in heterogeneous porous media
Simulating solute transport in heterogeneous porous media poses computational
challenges due to the high-resolution meshing required for traditional solvers. To overcome …
challenges due to the high-resolution meshing required for traditional solvers. To overcome …
Volume-preserving geometric shape optimization of the Dirichlet energy using variational neural networks
In this work, we explore the numerical solution of geometric shape optimization problems
using neural network-based approaches. This involves minimizing a numerical criterion that …
using neural network-based approaches. This involves minimizing a numerical criterion that …
Artificial neural networks as a natural tool in solution of variational problems in hydrodynamics
Artificial neural networks are a powerful tool for spatial and temporal functions
approximation. This study introduces a novel approach for modeling non-Newtonian fluid …
approximation. This study introduces a novel approach for modeling non-Newtonian fluid …
Data-driven hybrid modelling of waves at mid-frequencies range: Application to forward and inverse Helmholtz problems
In this paper, we introduce a novel hybrid approach that leverages both data and numerical
simulations to address the challenges of solving forward and inverse wave problems …
simulations to address the challenges of solving forward and inverse wave problems …
Volume-preserving geometric shape optimization of the Dirichlet energy using variational neural networks
In this work, we explore the numerical solution of geometric shape optimization problems
using neural network-based approaches. This involves minimizing a numerical criterion that …
using neural network-based approaches. This involves minimizing a numerical criterion that …
Ensemble deep neural network method for solving free boundary American style stochastic volatility models
We present an ensemble deep learning method for solving free boundary American-style
stochastic volatility models. Our solution framework for such free boundary problems …
stochastic volatility models. Our solution framework for such free boundary problems …
Learning-based geometric shape optimization of the Dirichlet energy
In this work, we explore the numerical solution of geometric shape optimization problems
using neural network-based approaches. This involves minimizing a numerical criterion that …
using neural network-based approaches. This involves minimizing a numerical criterion that …
[PDF][PDF] Volume-preserving physics-informed geometric shape optimization of the Dirichlet energy
In this work, we explore the numerical solution of geometric shape optimization problems
using neural network-based approaches. This involves minimizing a numerical criterion that …
using neural network-based approaches. This involves minimizing a numerical criterion that …
Deep Learning for Studying Materials Stability and Solving Thermodynamically Consistent PDES With Dynamic Boundary Conditions in Arbitrary Domains
C Li - 2023 - scholarcommons.sc.edu
Deep learning has achieved remarkable success in various fields, including image
processing, natural language processing, and signal processing, ushering in a …
processing, natural language processing, and signal processing, ushering in a …