Differential equations are commonly used to model dynamical deterministic systems in applications. When statistical parameter estimation is required to calibrate theoretical models to data, classical ...
WE have read Prof. Woolsey Johnson's work with some interest: his style is clear, and the worked-out examples well adapted to elucidate the points the writer wishes to bring out. He appears to ...
Efficient Solution of Nonlinear Ordinary Differential Equations Expressed in S-System Canonical Form
SIAM Journal on Numerical Analysis, Vol. 27, No. 3 (Jun., 1990), pp. 704-735 (32 pages) Ordinary differential equations can be recast into a nonlinear canonical form called an S-system. Evidence for ...
Solving ODEs analytically can be challenging, and therefore, several numerical methods have been developed to estimate their solutions. In recent years, machine learning techniques and neural networks ...
In contrast to the Euler method and the subsequent methods, we provide solutions to nonlinear ordinary differential equations. Consequently, our method does not require convergence. We apply our ...
Abstract: Deadlock detection for concurrent systems via static analysis is in general difficult because of state-space explosion; indeed it is PSPACE compete. This paper presents a new method to ...
ABSTRACT: We consider direct solution to third order ordinary differential equations in this paper. Method of collection and interpolation of the power series approximant of single variable is ...
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 ...
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