Enhancing computational fluid dynamics with machine learning
Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …
with numerous opportunities to advance the field of computational fluid dynamics. Here we …
Promising directions of machine learning for partial differential equations
Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain the …
PySINDy: A comprehensive Python package for robust sparse system identification
AA Kaptanoglu, BM de Silva, U Fasel… - ar** reduced-order models (ROMs) in the context of turbulent …
Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy
Time-dependent SOLPS-ITER simulations have been used to identify reduced models with
the sparse identification of nonlinear dynamics (SINDy) method and develop model …
the sparse identification of nonlinear dynamics (SINDy) method and develop model …