Overview of the Application of Physically Informed Neural Networks to the Problems of Nonlinear Fluid Flow in Porous Media
N Dieva, D Aminev, M Kravchenko, N Smirnov - Computation, 2024 - mdpi.com
To describe unsteady multiphase flows in porous media, it is important to consider the non-
Newtonian properties of fluids by including rheological laws in the hydrodynamic model …
Newtonian properties of fluids by including rheological laws in the hydrodynamic model …
Can ChatGPT be used for solving ordinary differential equations
In this research we have conducted empirical study to evaluate the capabilities of OpenAI's
chat bot ChatGPT for automated software code generation and programming numerical …
chat bot ChatGPT for automated software code generation and programming numerical …
Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks
Predicting the spatiotemporal data of natural processes is crucial for both academic
research and industrial applications. In particular, ice formation and melting processes play …
research and industrial applications. In particular, ice formation and melting processes play …
Towards true discovery of the differential equations
Differential equation discovery, a machine learning subfield, is used to develop interpretable
models, particularly in nature-related applications. By expertly incorporating the general …
models, particularly in nature-related applications. By expertly incorporating the general …
Towards discovery of the differential equations
AA Hvatov, RV Titov - Doklady Mathematics, 2023 - Springer
Abstract Differential equation discovery, a machine learning subfield, is used to develop
interpretable models, particularly, in nature-related applications. By expertly incorporating …
interpretable models, particularly, in nature-related applications. By expertly incorporating …