Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems. Learning the hidden physics within SDEs is crucial for unraveling fundamental ...

Quantum physics is a very diverse field: it describes particle collisions shortly after the Big Bang as well as electrons in ...

I find myself looking at everything as systems of equations – this person wants this, this thing needs to go there, we need money to do that thing – and thinking about how to put them together. We do ...

Abstract: Problems involving the classical linear partial differential equations of mathematical physics can be reduced to algebraic ones of a very much simpler structure by replacing the ...

Some questions in the exam will require you to recall and apply equations you have learned in class. Other questions will ask you to use the 'Physics Equation Sheet'. This is a set list of ...

You will be redirected to our submission process. In the past few years, fractional differential equations have emerged as a strong and well-organized mathematical tool in the study of many ...

Some questions in the exam will require you to recall and apply equations you have learned in class. Other questions will ask you to use the ‘Physics Equation Sheet’. This is a set list of ...

Abstract: Problems involving the classical linear partial differential equations of mathematical physics can be reduced to algebraic ones of a very much simpler structure by replacing the ...

Methods for solving linear, ordinary, and partial differential equations of mathematical physics. Green's functions, distribution theory, integral equations, transforms, potential theory, diffusion ...

Partial Differential Equations (PDEs) are central to both pure and applied mathematics. Any quantity which changes in space ...

This is the first part of a two course graduate sequence in analytical methods to solve ordinary and partial differential equations of mathematical physics. Review of Advanced ODE’s including power ...

Physics-informed neural networks (PINNs), rooted in deep learning, have emerged as a promising approach for solving partial differential equations (PDEs). By embedding the physical information ...