LASSIE: simulating large-scale models of biochemical systems on GPUs

A Tangherloni, MS Nobile, D Besozzi, G Mauri… - BMC …, 2017 - Springer
Background Mathematical modeling and in silico analysis are widely acknowledged as
complementary tools to biological laboratory methods, to achieve a thorough understanding …

[PDF][PDF] A family of modified backward differentiation formula (BDF) type block methods for the solution of stiff ordinary differential equations

K Atsi, GM Kumleng - International Journal of Statistics and Applied …, 2020 - academia.edu
A family of modified BDF type block methods for the solution stiff ordinary differential
equations has been constructed in this research. Four different block methods for step …

Numerical solution of first and second order differential equations with deep neural networks

TVSS Chaitanya, RP Kumar… - 2023 IEEE World …, 2023 - ieeexplore.ieee.org
Ordinary differential equations (ODEs) are a fundamental tool for modeling dynamical
systems in various scientific fields. However, solving ODEs analytically can be challenging …

Development of a fully implicit ODE-solver for containment analysis code

J Huang, J Li, Y Ma - Frontiers in Energy Research, 2024 - frontiersin.org
The thermal–hydraulic dynamics in containment are governed by a system of stiff ordinary
differential equations (ODEs). A fully implicit discretization scheme is adopted to discretize …

A family of matrix coefficient formulas for solving ordinary differential equations

SY Chang - Applied Mathematics and Computation, 2022 - Elsevier
A matrix form of coefficients is applied to develop a new family of one-step explicit methods.
Clearly, this type of methods is different from the conventional methods that have scalar …

Incorporating NODE with pre-trained neural differential operator for learning dynamics

S Gong, Q Meng, Y Wang, L Wu, W Chen, Z Ma, TY Liu - Neurocomputing, 2023 - Elsevier
Learning dynamics governed by differential equations is crucial for predicting and
controlling the systems in science and engineering. Neural Ordinary Differential Equation …

[PDF][PDF] An explicit time-step** method based on error minimization for solving stiff system of ordinary differential equations

M Rahmanzadeh, M Barfeie - Malaysian Journal of Mathematical …, 2018 - mjms.upm.edu.my
In this paper, an explicit one step method is presented for numerical solution of stiff systems
of ordinary differential equations (ODEs). In this method, the solution of the ODE is …

Solution approaches to differential equations of mechanical system dynamics: a case study of car suspension system

TO Terefe, HG Lemu - Advances in Science and Technology …, 2018 - yadda.icm.edu.pl
Solution of a dynamic system is commonly demanding when analytical approaches are
used. In order to solve numerically, describing the motion dynamics using differential …

Procedure for Exact Solutions of Sti Ordinary Dierential Equations Systems

B Benhammouda… - British Journal of …, 2014 - archive.submissionwrite.com
In this work, we present a technique for the analytical solution of systems of sti ordinary
dierential equations (SODEs) using the power series method (PSM). Three SODEs systems …

An optimized 5-point block formula for direct numerical solution of first order stiff initial value problems

VO Atabo, PO Olatunji - NIGERIAN ANNALS OF PURE AND APPLIED …, 2020 - napas.org.ng
In this research article, we focus on the formulation of a 5-point block formula for solving first
order ordinary differential equations (ODEs). The method is formulated via interpolation and …