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Concept# Differential equation

Summary

In mathematics, a differential equation is an equation that relates one or more unknown functions and their derivatives. In applications, the functions generally represent physical quantities, the derivatives represent their rates of change, and the differential equation defines a relationship between the two. Such relations are common; therefore, differential equations play a prominent role in many disciplines including engineering, physics, economics, and biology.
The study of differential equations consists mainly of the study of their solutions (the set of functions that satisfy each equation), and of the properties of their solutions. Only the simplest differential equations are soluble by explicit formulas; however, many properties of solutions of a given differential equation may be determined without computing them exactly.
Often when a closed-form expression for the solutions is not available, solutions may be approximated numerically using computers. The theory of dynami

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In mathematics, a partial differential equation (PDE) is an equation which computes a function between various partial derivatives of a multivariable function.
The function is often thought of as

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In mathematics, the derivative shows the sensitivity of change of a function's output with respect to the input. Derivatives are a fundamental tool of calculus. For example, the derivative of the p

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We study the system of linear partial differential equations given by dw + a Lambda w = f, on open subsets of R-n, together with the algebraic equation da Lambda u = beta, where a is a given 1-form, f is a given (k + 1)-form, beta is a given k + 2-form, w and u are unknown k-forms. We show that if rank[da] >= 2(k+1) those equations have at most one solution, if rank[da] equivalent to 2m >= 2(k + 2) they are equivalent with beta = df + a Lambda f and if rank[da] equivalent to 2m >= 2(n - k) the first equation always admits a solution. Moreover, the differential equation is closely linked to the Poincare lemma. Nevertheless, as soon as a is nonexact, the addition of the term a Lambda w drastically changes the problem.

2018This thesis focuses on the numerical analysis of partial differential equations (PDEs) with an emphasis on first and second-order fully nonlinear PDEs. The main goal is the design of numerical methods to solve a variety of equations such as orthogonal maps, the prescribed Jacobian equation and inequality, the elliptic and parabolic Monge-Ampère equations.
For orthogonal map we develop an \emph{operator-splitting/finite element} approach for the numerical solution of the Dirichlet problem. This approach is built on the variational principle, the introduction of an associated flow problem, and a time-stepping splitting algorithm. Moreover, we propose an extension of this method with an \emph{anisotropic mesh adaptation algorithm}. This extension allows us to track singularities of the solution's gradient more accurately. Various numerical experiments demonstrate the accuracy and the robustness of the proposed method for both constant and adaptive mesh.
For the prescribed Jacobian equation and the three-dimensional Monge-Ampère equation, we consider a \emph{least-squares/relaxation finite element method} for the numerical solution of the Dirichlet problems. We then introduce a relaxation algorithm that splits the least-square problem, which stems from a reformulation of the original equations, into local nonlinear and variational problems. We develop dedicated solvers for the algebraic problems based on Newton method and we solve the differential problems using mixed low-order finite element method. Overall the least squares approach exhibits appropriate convergence orders in $L^2(\Omega)$ and $H^1(\Omega)$ error norms for various numerical tests.
We also design a \emph{second-order time integration method} for the approximation of a parabolic two-dimensional Monge-Ampère equation. The space discretization of this method is based on low-order finite elements, and the time discretization is achieved by the implicit Crank-Nicolson type scheme.
We verify the efficiency of the proposed method on time-dependent and stationary problems. The results of numerical experiments show that the method achieves nearly optimal orders for the $L^2(\Omega)$ and $H^1(\Omega)$ error norms when smooth solutions are approximated.
Finally, we present an adaptive mesh refinement algorithm for the elliptic Monge-Ampere equation based on the residual error estimate. The robustness of the proposed algorithm is verified using various test cases and two different solvers which are inspired by the two previous proposed numerical methods.

For the numerical simulation of the circulatory system, geometrical multiscale models based on the coupling of systems of differential equations with different spatial dimensions are becoming common practice. In this paper we address the mathematical analysis of a coupled multiscale system involving a zero-dimensional (0D) model, describing the global characteristics of the circulatory system, and a one-dimensional (1D) model giving the pressure propagation along a straight vessel. We provide a local-in-time existence and uniqueness of classical solutions for this coupled problem. To this purpose we reformulate the original problem in a general abstract framework by splitting it into subproblems (the 0D system of ODEs and the 1D hyperbolic system of PDEs); then we use fixed-point techniques. The abstract result is then applied to the original blood flow case under very realistic hypotheses on the data.

2005