How to solve computational science problems with AI: Physics-Informed Neural Networks (PINNs)
In today’s world, numerous challenges exist, particularly in computational science. This post focuses on solving scientific problems through simulations, such as computational physics. These simulations are essential for advancing our understanding of complex systems and improving various technologies.
To understand the framework, we first need a clear vision of Partial Differential Equations (PDEs). Then, we will provide a brief overview of Physics-Informed Neural Networks (PINNs) and their implementation.
Multivariable Calculus Multivariable calculus extends single-variable calculus to functions with more than one variable. It involves studying functions that depend on multiple variables and covers concepts such as partial derivatives.
Partial derivatives refer to taking the derivative of a function that depends on more than one variable with respect to one variable while keeping the other variables constant. Partial derivatives play an important role in multivariable calculus and help us understand how functions change.
