Element learning: a systematic approach of accelerating finite element-type methods via machine learning, with applications to radiative transfer

In this paper, we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations (PDEs). The main idea is to use a neural network to learn the solution map of the PDEs and to do so in an element-wise fashion. This map takes input of the element geometry and the PDEs' parameters on that element, and gives output of two operators -- (1) the in2out operator for inter-element communication, and (2) the in2sol operator (Green's function) for element-wise solution recovery. A significant advantage of this approach is that, once trained, this network can be used for the numerical solution of the PDE for any domain geometry and any parameter distribution without retraining. Also, the training is significantly simpler since it is done on the element level instead on the entire domain. We call this approach element learning. This method is closely related to hybridizbale discontinuous Galerkin (HDG) methods in the sense that the local solvers of HDG are replaced by machine learning approaches. Numerical tests are presented for an example PDE, the radiative transfer equation, in a variety of scenarios with idealized or realistic cloud fields, with smooth or sharp gradient in the cloud boundary transition. Under a fixed accuracy level of $10^{-3}$ in the relative $L^2$ error, and polynomial degree $p=6$ in each element, we observe an approximately 5 to 10 times speed-up by element learning compared to a classical finite element-type method

Generalized Projection-Based Error Analysis of Hybridizable Discontinuous Galerkin Methods

For some hybridizable discontinuous Galerkin (HDG) methods, suitably devised projections make their analyses simple and concise. However, devising these projections is usually difficult and many important HDG methods still lack their corresponding projections; consequently, their analyses become cumbersome. In this thesis, we propose novel analytical tools to solve this problem. These tools can be used to systematically devise and analyze new HDG methods, to unify their analyses, and to simplify and improve existing ones. We shall study these tools and their applications in three cases: (1) HDG methods for elastic problems, (2) HDG methods on polyhedral meshes, and (3) HDG methods for Maxwell equations. They will be discussed in Chapter 2, Chapter 3, and Chapter 4, respectively. In Chapter 1, we give an introduction to motivate the topic of this thesis. Finally in Chapter 5, we conclude by discussing several promising potential developments of our work

An invitation to the theory of the hybridizable discontinuous Galerkin method: projections, estimates, tools

This monograph requires basic knowledge of the variational theory of elliptic PDE and the techniques used for the analysis of the Finite Element Method. However, all the tools for the analysis of FEM (scaling arguments, finite dimensional estimates in the reference configuration, Piola transforms) are carefully introduced before being used, so that the reader does not need to go over longforgotten textbooks. Readers include: computational mathematicians, numerical analysts, engineers and scientists interested in new and computationally competitive Discontinuous Galerkin methods. The intended audience includes graduate students in computational mathematics, physics, and engineering, since the prerequisites are quite basic for a second year graduate student who has already taken a non necessarily advanced class in the Finite Element method

A Universal Predictor‐Corrector Approach for Minimizing Artifacts Due To Mesh Refinement

Abstract With nested grids or related approaches, it is known that numerical artifacts can be generated at the interface of mesh refinement. Most of the existing methods of minimizing these artifacts are either problem‐dependent or numerical methods‐dependent. In this paper, we propose a universal predictor‐corrector approach to minimize these artifacts. By its construction, the approach can be applied to a wide class of models and numerical methods without modifying the existing methods but instead incorporating an additional step. The idea is to use an additional grid setup with a refinement interface at a different location, and then to correct the predicted state near the refinement interface by using information from the other grid setup. We give some analysis for our method in the setting of a one‐dimensional advection equation, showing that the key to the success of the method depends on an optimized way of choosing the weight functions, which determine the strength of the corrector at a certain location. Furthermore, the method is also tested in more general settings by numerical experiments, including shallow water equations, multi‐dimensional problems, and a variety of underlying numerical methods including finite difference/finite volume and spectral element. Numerical tests suggest the effectiveness of the method on reducing numerical artifacts due to mesh refinement

error analysis of new semidiscrete, Hamiltonian HDG methods for the time-dependent Maxwell’s equations

We present the first a priori error analysis of a class of space-discretizations by Hybridizable Discontinuous Galerkin (HDG) methods for the time-dependent Maxwell’s equations introduced in Sánchez et al. [Comput. Methods Appl. Mech. Eng. 396 (2022) 114969]. The distinctive feature of these discretizations is that they display a discrete version of the Hamiltonian structure of the original Maxwell’s equations. This is why they are called ``Hamiltonian’’ HDG methods. Because of this, when combined with symplectic time-marching methods, the resulting methods display an energy that does not drift in time. We provide a single analysis for several of these methods by exploiting the fact that they only differ by the choice of the approximation spaces and the stabilization functions. We also introduce a new way of discretizing the static Maxwell’s equations in order to define the initial condition in a manner consistent with our technique of analysis. Finally, we present numerical tests to validate our theoretical orders of convergence and to explore the convergence properties of the method in situations not covered by our analysis

Memristor-based time-delay chaotic system with hidden extreme multi-stability and pseudo-random sequence generator

At present, researchers only find that memristor-based time-delay (MBTD) chaotic systems have rich dynamic behaviors. However, there are still many difficulties in the analysis and application of the MBTD chaotic system. Therefore, a novel 3-D MBTD chaotic system with extreme multi-stability and line equilibrium points is proposed in this work. First, by applying dynamic analysis and numerical simulation, basic behaviors of the system are employed to illustrate the superiority of the system. Then, some special nonlinear phenomena are observed by two-parameter bifurcation diagrams, such as coexisting hidden attractors of both periodic and chaotic, coexisting multi-scroll hidden attractors, coexisting single-scroll hidden attractors and coexisting periodic attractors, which mean that hidden extreme multi-stability occurs. Moreover, the system which has good autocorrelation and cross-correlation is applied to generate the chaotic pseudo-random sequence. Finally, the approximate entropy of the chaotic pseudo-random sequence is larger than that of other time-delay chaotic systems. The above manifest that this MBTD chaotic system possesses abundant dynamics and good randomness. Hence this system owns latent force in the application of memristor