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 ...

Abstract: We investigate the feasibility of deriving surrogate ordinary differential equations (ODEs) from individual-based models (IBMs) simulated in the PhysiCell platform. As a preliminary ...

A method for the numerical solution of the n-point boundary value problem for homogeneous linear ordinary differential equations is developed. The method requires two Runge-Kutta integrations over the ...

This advanced course explores the theory, development, and application of exponential integrators in the numerical solution of stiff systems of ordinary differential equations (ODEs). The key topics ...

A numerical method is presented for the solution of boundary value problems involving linear ordinary differential equations. The method described is noniterative and makes use of any one-step ...

Whatever else can be said about the trial of Daniel Penny, it must be said that it was the worst advertisement for public transit since the invention of the wheel. Penny was riding on a train in New ...

1 Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States 2 Department of Psychiatry and Behavioral Neuroscience, University of ...

Abstract: Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their ...

ABSTRACT: In this article, we will explore the applications of linear ordinary differential equations (linear ODEs) in Physics and other branches of mathematics, and dig into the matrix method for ...

1 Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany 2 CIBSS - Centre for Integrative Biological Signaling Studies, ...