This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...

Abstract: In this paper, we have developed an artificial neural network (ANN) method for finding solutions to the Dirichlet problem for fractional order differential equations (FODEs) 0 α1 using the ...

Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands The rapid growth of large-scale neuroscience datasets has spurred diverse modeling strategies, ranging ...

remove-circle Internet Archive's in-browser bookreader "theater" requires JavaScript to be enabled. It appears your browser does not have it turned on. Please see ...

A large class of problems leading to digital computer processing can be formulated in terms of the numerical solution of systems of ordinary differential equations. Powerful methods are in existence ...

A math word problem is a narrative with a specific topic that provides clues to the correct equation with numerical quantities and variables therein. In this paper, we focus on the task of generating ...

Practical word problems are often considered one of the most complex parts of elementary school mathematics. They require students to understand the context of a math problem, identify what the ...

Engineers design safer cars, more resilient spacecraft, and stronger bridges using complex math problems that drive the underlying processes. Similarly, doctors use mathematical models to predict ...

In the realm of financial mathematics, differential equations play a pivotal role in modeling and solving problems related to various financial instruments and markets. These mathematical tools are ...

Simo Särkkä and Arno Solin (2019). Applied Stochastic Differential Equations. Cambridge University Press. Cambridge, UK. The book can be ordered through Cambridge University Press or, e.g., from ...