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This article revisits the approximation problem of systems of nonlinear delay differential equations (DDEs) by a set of ordinary differential equations (ODEs). We work in Hilbert spaces endowed with a natural inner product including a point…

Chaotic Dynamics · Physics 2015-09-11 Mickaël D. Chekroun , Michael Ghil , Honghu Liu , Shouhong Wang

Solving partial differential equations (PDEs) with highly oscillatory solutions on complex domains remains a challenging and important problem. High-frequency oscillations and intricate geometries often result in prohibitively expensive…

Numerical Analysis · Mathematics 2025-10-28 Gareth Hardwick , Haizhao Yang

Transformations of differential equations to other equivalent equations play a central role in many routines for solving intricate equations. A class of differential equations that are particularly amenable to solution techniques based on…

Classical Analysis and ODEs · Mathematics 2020-05-21 Winter Sinkala

In this paper, a high-order and fast numerical method is investigated for the time-fractional Black-Scholes equation. In order to deal with the typical weak initial singularities of the solution, we construct a finite difference scheme with…

Numerical Analysis · Mathematics 2021-09-09 Kerui Song , Pin Lyu

Discovering governing equations from data is critical for diverse scientific disciplines as they can provide insights into the underlying phenomenon of dynamic systems. This work presents a new representation for governing equations by…

Machine Learning · Computer Science 2022-06-03 Hongpeng Zhou , Wei Pan

The well-posedness of a class of optimal control problems is analysed, where the state equation couples a nonlinear degenerate Fokker-Planck equation with a system of Ordinary Differential Equations (ODEs). Such problems naturally arise as…

Optimization and Control · Mathematics 2024-11-01 Francesca Anceschi , Giacomo Ascione , Daniele Castorina , Francesco Solombrino

This paper is dedicated to the construction of high-order (in both space and time) finite-difference schemes for both forward and backward PDEs and PIDEs, such that option prices obtained by solving both the forward and backward equations…

Computational Finance · Quantitative Finance 2014-03-10 Andrey Itkin

Partial differential equations (PDEs) are widely used for modeling various physical phenomena. These equations often depend on certain parameters, necessitating either the identification of optimal parameters or the solution of the…

Numerical Analysis · Mathematics 2025-10-17 Martina Bukač , Iva Manojlović , Boris Muha , Domagoj Vlah

We propose machine learning methods for solving fully nonlinear partial differential equations (PDEs) with convex Hamiltonian. Our algorithms are conducted in two steps. First the PDE is rewritten in its dual stochastic control…

Computational Finance · Quantitative Finance 2022-05-23 William Lefebvre , Grégoire Loeper , Huyên Pham

The Fractional Diffusion Equation (FDE) is a mathematical model that describes anomalous transport phenomena characterized by non-local and long-range dependencies which deviate from the traditional behavior of diffusion. Solving this…

Numerical Analysis · Mathematics 2023-11-14 Mohammad Partohaghighi , Emmanuel Asante-Asamani , Olaniyi S. Iyiola

The Cahn-Hilliard equation is a fundamental model for describing phase separation phenomena in binary mixtures. Traditional numerical methods, such as finite difference and finite element methods, often incur substantial computational cost,…

Numerical Analysis · Mathematics 2026-05-26 Yi Liu , Shuting Gu

Ordinary differential equations (ODEs) are a conventional way to describe the observed dynamics of physical systems. Scientists typically hypothesize about dynamical behavior, propose a mathematical model, and compare its predictions to…

Machine Learning · Computer Science 2025-11-20 Nils Wildt , Daniel M. Tartakovsky , Sergey Oladyshkin , Wolfgang Nowak

Mechanistic models with differential equations are a key component of scientific applications of machine learning. Inference in such models is usually computationally demanding, because it involves repeatedly solving the differential…

Machine Learning · Statistics 2022-07-06 Jonathan Schmidt , Nicholas Krämer , Philipp Hennig

This work introduces a new approach for accelerating the numerical analysis of time-domain partial differential equations (PDEs) governing complex physical systems. The methodology is based on a combination of a classical reduced-order…

Machine Learning · Computer Science 2024-06-06 Victor Matray , Faisal Amlani , Frédéric Feyel , David Néron

We propose a general strategy for reduced order modeling of systems that display highly nonlinear oscillations. By considering a continuous family of forced periodic orbits defined in relation to a stable fixed point and subsequently…

Dynamical Systems · Mathematics 2023-02-07 Dan Wilson , Kai Sun

In this short paper we identify special systems of (an arbitrary number) N of first-order Difference Equations with nonlinear homogeneous polynomials of arbitrary degree M in their right-hand sides, which feature very simple explicit…

Dynamical Systems · Mathematics 2021-09-28 Francesco Calogero , Farrin Payandeh

This paper studies the expressive and computational power of discrete Ordinary Differential Equations (ODEs), a.k.a. (Ordinary) Difference Equations. It presents a new framework using these equations as a central tool for computation and…

Logic in Computer Science · Computer Science 2022-09-27 Olivier Bournez , Arnaud Durand

Many engineered physical processes exhibit nonlinear but asymptotically stable dynamics that converge to a finite set of equilibria determined by control inputs. Identifying such systems from data is challenging: stable dynamics provide…

Systems and Control · Electrical Eng. & Systems 2026-03-31 Ike Griss Salas , Ethan King

A point process for event arrivals in high frequency trading is presented. The intensity is the product of a Hawkes process and high dimensional functions of covariates derived from the order book. Conditions for stationarity of the process…

Trading and Market Microstructure · Quantitative Finance 2026-05-12 Luca Mucciante , Alessio Sancetta

This paper concerns the numerical procedure for solving hybrid optimal control problems with sliding modes. The proposed procedure has several features which distinguishes it from the other procedures for the problem. First of all a sliding…

Optimization and Control · Mathematics 2021-01-18 Radoslaw Pytlak , Damian Suski
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