[HTML][HTML] Machine learning for polymer composites process simulation–a review
Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …
applications in the fields of engineering and computer science. In the field of material …
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
A composable machine-learning approach for steady-state simulations on high-resolution grids
In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable
Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher …
Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher …
Differentiable Programming for Computational Plasma Physics
NB McGreivy - 2024 - search.proquest.com
Differentiable programming allows for derivatives of functions implemented via computer
code to be calculated automatically. These derivatives are calculated using automatic …
code to be calculated automatically. These derivatives are calculated using automatic …
A composable autoencoder-based algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE)
to model various physical processes. Traditional PDE solvers are very accurate but …
to model various physical processes. Traditional PDE solvers are very accurate but …