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This article presents an axiomatic approach for deductive verification of existence and liveness for ordinary differential equations (ODEs) with differential dynamic logic (dL). The approach yields proofs that the solution of a given ODE…

Logic in Computer Science · Computer Science 2021-09-08 Yong Kiam Tan , André Platzer

Stability is required for real world controlled systems as it ensures that those systems can tolerate small, real world perturbations around their desired operating states. This paper shows how stability for continuous systems modeled by…

Logic in Computer Science · Computer Science 2022-02-25 Yong Kiam Tan , André Platzer

Ordinary differential equations (ODEs) are foundational in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different…

Methodology · Statistics 2023-09-01 Itai Dattner , Shota Gugushvili , Oleksandr Laskorunskyi

Ordinary differential equations (ODEs) are the primary means to modelling dynamical systems in many natural and engineering sciences. The number of equations required to describe a system with high heterogeneity limits our capability of…

Mathematical Software · Computer Science 2017-07-17 Andrea Vandin

The method and the formula of variation of constants for ordinary differential equations (ODEs) is a fundamental tool to analyze the dynamics of an ODE near an equilibrium. It is natural to expect that such a formula works for delay…

Dynamical Systems · Mathematics 2023-10-17 Junya Nishiguchi

An efficient approximate version of implicit Taylor methods for initial-value problems of systems of ordinary differential equations (ODEs) is introduced. The approach, based on an approximate formulation of Taylor methods, produces a…

Numerical Analysis · Mathematics 2024-02-05 Antonio Baeza , Raimund Bürger , María del Carmen Martí , Pep Mulet , David Zorío

Ordinary differential equations (ODEs) are widely used to model dynamical behavior of systems. It is important to perform identifiability analysis prior to estimating unknown parameters in ODEs (a.k.a. inverse problem), because if a system…

Optimization and Control · Mathematics 2021-03-11 Xing Qiu , Tao Xu , Babak Soltanalizadeh , Hulin Wu

We develop symbolic methods of asymptotic approximations for solutions of linear ordinary differential equations and use to them stabilize numerical calculations. Our method follows classical analysis for first-order systems and…

Symbolic Computation · Computer Science 2011-10-12 Christopher J. Winfield

We explore the use of liveness for interactive program verification for a simple concurrent object language. Our experimental IDE integrates two (formally dual) kinds of continuous testing into the development environment:…

Programming Languages · Computer Science 2016-09-15 Roly Perera , Simon J. Gay

Purely numerical methods do not always provide an accurate way to find all the global solutions to nonlinear ODE on infinite intervals. For example, finite-difference methods fail to capture the asymptotic behavior of solutions, which might…

Classical Analysis and ODEs · Mathematics 2007-10-01 Michael Robinson

This work establishes a rigorous connection between stability properties of discrete-time algorithms (DTAs) and corresponding continuous-time dynamical systems derived through $ O(s^r) $-resolution ordinary differential equations (ODEs). We…

Optimization and Control · Mathematics 2026-03-03 Amir Ali Farzin , Yuen-Man Pun , Philipp Braun , Iman Shames

The axiomatic theory of ordinary differential equations, owing to its simplicity, can provide a useful framework to describe various generalizations of dynamical systems. In this study, we consider how dynamical properties can be…

Dynamical Systems · Mathematics 2024-02-06 Tomoharu Suda

Continuous deep learning models, referred to as Neural Ordinary Differential Equations (Neural ODEs), have received considerable attention over the last several years. Despite their burgeoning impact, there is a lack of formal analysis…

Machine Learning · Computer Science 2022-07-15 Diego Manzanas Lopez , Patrick Musau , Nathaniel Hamilton , Taylor T. Johnson

The understanding and modeling of complex physical phenomena through dynamical systems has historically driven scientific progress, as it provides the tools for predicting the behavior of different systems under diverse conditions through…

Machine Learning · Computer Science 2025-10-03 Karin L. Yu , Eleni Chatzi , Georgios Kissas

This paper explores the critical role of differentiation approaches for data-driven differential equation discovery. Accurate derivatives of the input data are essential for reliable algorithmic operation, particularly in real-world…

Machine Learning · Computer Science 2023-11-13 Mikhail Masliaev , Ilya Markov , Alexander Hvatov

In this paper we introduce and investigate a new kind of functional (including ordinary and evolutionary partial) differential equations. The main goal of this paper is to explore our new philosophy by some examples on functional ODEs and…

Analysis of PDEs · Mathematics 2014-02-14 De-Xing Kong , Cheng Zhang

In this paper we study the stability of explicit finite difference discretizations of linear advection-diffusion equations (ADE) with arbitrary order of accuracy in the context of method of lines. The analysis first focuses on the stability…

Numerical Analysis · Mathematics 2020-06-17 Xianyi Zeng , Md Mahmudul Hasan

We present a novel approach that redefines the traditional interpretation of explicit and implicit discretization methods for solving a general class of advection-diffusion equations (ADEs) featuring nonlinear advection, diffusion…

Analysis of PDEs · Mathematics 2025-06-10 Amin Jafarimoghaddam , Manuel Soler , Irene Ortiz

Differential equations are frequently used in engineering domains, such as modeling and control of industrial systems, where safety and performance guarantees are of paramount importance. Traditional physics-based modeling approaches…

Systems and Control · Electrical Eng. & Systems 2020-11-30 Aaron Tuor , Jan Drgona , Draguna Vrabie

Ordinary differential equations (ODEs) are widely used to model biological, (bio-)chemical and technical processes. The parameters of these ODEs are often estimated from experimental data using ODE-constrained optimisation. This article…

Optimization and Control · Mathematics 2015-11-06 Anna Fiedler , Fabian J. Theis , Jan Hasenauer
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