Physics-informed neural networks were tested for their capabilities in predicting concentration profiles in gradient liquid ...

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and ...

The aim of this course is to provide an introduction to modern methods for studying nonlinear partial differential equations. The content of the course, which can change from time to time, is built ...

The study of wave phenomena by means of mathematical models often leads to a certain class of nonlinear partial differential equations referred to as integrable systems. My main area of research deals ...