Newton's method: A link between continuous and discrete solutions of nonlinear problems

Newton's method for nonlinear mechanics problems replaces the governing nonlinear equations by an iterative sequence of linear equations. When the linear equations are linear differential equations, the equations are usually solved by numerical methods. The iterative sequence in Newton's method can exhibit poor convergence properties when the nonlinear problem has multiple solutions for a fixed set of parameters, unless the iterative sequences are aimed at solving for each solution separately. The theory of the linear differential operators is often a better guide for solution strategies in applying Newton's method than the theory of linear algebra associated with the numerical analogs of the differential operators. In fact, the theory for the differential operators can suggest the choice of numerical linear operators. In this paper the method of variation of parameters from the theory of linear ordinary differential equations is examined in detail in the context of Newton's method to demonstrate how it might be used as a guide for numerical solutions

A Fast Algorithm for Parabolic PDE-based Inverse Problems Based on Laplace Transforms and Flexible Krylov Solvers

We consider the problem of estimating parameters in large-scale weakly nonlinear inverse problems for which the underlying governing equations is a linear, time-dependent, parabolic partial differential equation. A major challenge in solving these inverse problems using Newton-type methods is the computational cost associated with solving the forward problem and with repeated construction of the Jacobian, which represents the sensitivity of the measurements to the unknown parameters. Forming the Jacobian can be prohibitively expensive because it requires repeated solutions of the forward and adjoint time-dependent parabolic partial differential equations corresponding to multiple sources and receivers. We propose an efficient method based on a Laplace transform-based exponential time integrator combined with a flexible Krylov subspace approach to solve the resulting shifted systems of equations efficiently. Our proposed solver speeds up the computation of the forward and adjoint problems, thus yielding significant speedup in total inversion time. We consider an application from Transient Hydraulic Tomography (THT), which is an imaging technique to estimate hydraulic parameters related to the subsurface from pressure measurements obtained by a series of pumping tests. The algorithms discussed are applied to a synthetic example taken from THT to demonstrate the resulting computational gains of this proposed method

A collage-based approach to solving inverse problems for second-order nonlinear hyperbolic PDEs

A goal of many inverse problems is to find unknown parameter values, \u3bb 08 \u39b, so that the given observed data utrue agrees well with the solution data produced using these parameters u\u3bb. Unfortunately finding u\u3bb in terms of the parameters of the problem may be a difficult or even impossible task. Further, the objective function may be a complicated function of the parameters \u3bb 08 \u39b and may require complex minimization techniques. In recent literature, the collage coding approach to solving inverse problems has emerged. This approach avoids the aforementioned difficulties by bounding the approximation error above by a more readily minimizable distance, thus making the approximation error small. The first of these methods was applied to first-order ordinary differential equations and gets its name from the \u201ccollage theorem\u201d used in this setting to achieve an upperbound on the approximation error. A number of related ODE problems have been solved using this method and extensions thereof. More recently, collage-based methods for solving linear and nonlinear elliptic partial differential equations have been developed. In this paper we establish a collage-based method for solving inverse problems for nonlinear hyperbolic PDEs. We develop the necessary background material, discuss the complications introduced by the presence of time-dependence, establish sufficient conditions for using the collage-based approach in this setting and present examples of the theory in practice

Applied Dynamics In School And Practice

Mechanical engineering, mechanical engineering technology, and related educational programs are not addressing in a sufficient way the principles associated with applying analytical investigations in solving actual engineering problems. Because of this, graduates do not have the adequate skills required to use the methods of applied dynamics in the process of analyzing mechanical systems. These methods allow one to obtain an understanding of the role of the parameters of a system and to carry out a purposeful control of the values of these parameters with the goal to achieve the desired performance. Engineering and engineering technology programs pay very little attention to addressing these steps. It should be stressed that these programs do not offer a universal straightforward methodology of solving linear differential equations of motion that allow revealing all important interrelationships between the aspects of the engineering problem. It is difficult to formulate the reasons why there is such a low interest in applying the analytical approach in order to reveal the interrelationships between decisive aspects of the operational process of an engineering system in order to achieve the desired goal. Actually, there is almost a complete silence with regard to this issue. Hence, we assume that the first reason could be that there is no recognition of the existence of such a problem. In other words, there is no need to apply these analytical methods since these methods are not beneficial. We do not believe that the engineering community supports this reason. It is not a matter of demonstrating factual data that show how many times the theory was helpful. Without the support of the theory we cannot justifiably evaluate the results of our solutions. If we agree that there is problem, then why are there no publications that would stimulate discussions leading toward a solution of the problem? Here is the second reason. Until now, engineering programs do not present the straightforward universal theoretically sound methodologies for solving the second order linear differential equations that are vital for mechanical and electrical engineering. Without any suggestions of how to solve this problem, it did not make much sense to begin a discussion. In our opinion, this is why we have silence with the regard to this problem. However, it is well known that Laplace Transforms allow solving any linear differential equation of motion. It is justifiable to assume that the main reason why the Laplace Transform methodology is not adopted by learning environments consists in the absence of the majority of tables of Laplace Transform Pairs that are needed for solving differential equations of motion as well as differential equations describing electrical circuits. However, the situation is changed. Current publications comprise the adequate tables that are needed for solving linear differential equations of motion associated with all common mechanical engineering problems. Practicing engineers and students need assistance in acquiring the knowledge of composing differential equations of motion. They need certain training in solving these equations using Laplace Transform methodology. Several recommendations are proposed on how to expedite the implementation in academia and in industry of the methods of applied dynamics in solving common mechanical engineering problems

A FAMILY OF EXPONENTIALLY FITTED MULTIDERIVATIVE METHOD FOR STIFF DIFFERENTIAL EQUATIONS

In this paper, an A-stable exponentially fitted predictor-corrector using multiderivative linear multistep method for solving stiff differential equations is developed. The method which is a two-step third derivative method of order five contains free parameters. The numerical stability analysis of the method was discussed, and found to be A-stable. Numerical examples are provided to show the efficiency of the method when compared with existing methods in the literature that have solved the set of problems

A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm

Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization problems. DE has many qualities in its hand, which are attributing to its popularity. DE also is known for its simplicity in solving the given problem with few control parameters: the population size (NP), the mutation rate (F) and the crossover rate (Cr). To avoid the difficulty involved in setting of suitable values for NP, F and Cr many parameter adaptation strategies are proposed in the literature. This paper is to present the working principle of the parameter adaptation strategies of F and Cr. The adaptation strategies are categorized based on the logic used by the authors, and clear insights about all the categories are presented

A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks

Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible. While global minimization of the PDE residual over the network parameters works well for boundary value problems, catastrophic forgetting impairs the applicability of this approach to initial value problems (IVPs). In an alternative local-in-time approach, the optimization problem can be converted into an ordinary differential equation (ODE) on the network parameters and the solution propagated forward in time; however, we demonstrate that current methods based on this approach suffer from two key issues. First, following the ODE produces an uncontrolled growth in the conditioning of the problem, ultimately leading to unacceptably large numerical errors. Second, as the ODE methods scale cubically with the number of model parameters, they are restricted to small neural networks, significantly limiting their ability to represent intricate PDE initial conditions and solutions. Building on these insights, we develop Neural IVP, an ODE based IVP solver which prevents the network from getting ill-conditioned and runs in time linear in the number of parameters, enabling us to evolve the dynamics of challenging PDEs with neural networks.Comment: ICLR 2023. Code available at https://github.com/mfinzi/neural-iv

Analytic calculation of energies and wave functions of the quartic and pure quartic oscillators

Ground state energies and wave functions of quartic and pure quartic oscillators are calculated by first casting the Schr\"{o}dinger equation into a nonlinear Riccati form and then solving that nonlinear equation analytically in the first iteration of the quasilinearization method (QLM). In the QLM the nonlinear differential equation is solved by approximating the nonlinear terms by a sequence of linear expressions. The QLM is iterative but not perturbative and gives stable solutions to nonlinear problems without depending on the existence of a smallness parameter. Our explicit analytic results are then compared with exact numerical and also with WKB solutions and it is found that our ground state wave functions, using a range of small to large coupling constants, yield a precision of between 0.1 and 1 percent and are more accurate than WKB solutions by two to three orders of magnitude. In addition, our QLM wave functions are devoid of unphysical turning point singularities and thus allow one to make analytical estimates of how variation of the oscillator parameters affects physical systems that can be described by the quartic and pure quartic oscillators.Comment: 8 pages, 12 figures, 1 tabl