Solutions exhibiting weak initial singularities arise in various equations, including diffusion and subdiffusion equations. When employing the well-known L1 scheme to solve subdiffusion equations with weak singularities, numerical simulations reveal that this scheme exhibits varying convergence rates for different choices of model parameters (i.e., domain size, final time $T$, and reaction coefficient $κ$). This elusive phenomenon is not unique to the L1 scheme but is also observed in other numerical methods for reaction-diffusion equations such as the backward Euler (IE) scheme, Crank-Nicolson (C-N) scheme, and two-step backward differentiation formula (BDF2) scheme. The existing literature lacks an explanation for the existence of two different convergence regimes, which has puzzled us for a long while and motivated us to study this inconsistency between the standard convergence theory and numerical experiences. In this paper, we provide a general methodology to systematically obtain error estimates that incorporate the exponential decaying feature of the solution. We term this novel error estimate the `decay-preserving error estimate' and apply it to the aforementioned IE, C-N, and BDF2 schemes. Our decay-preserving error estimate consists of a low-order term with an exponential coefficient and a high-order term with an algebraic coefficient, both of which depend on the model parameters. Our estimates reveal that the varying convergence rates are caused by a trade-off between these two components in different model parameter regimes. By considering the model parameters, we capture different states of the convergence rate that traditional error estimates fail to explain. This approach retains more properties of the continuous solution. We validate our analysis with numerical results.
A high-order nodally bound-preserving and mass-conservative method for linear fourth-order elliptic problems and its applications to nonlinear parabolic equations
A Structure-Preserving Framework for Solving Parabolic Partial Differential Equations with Neural Networks
Department of Applied Mathematics, The Hong Kong Polytechnic University（应用数学系，香港Polytechnic大学）；Department of Mathematics, University of South Carolina（数学系，南卡罗来纳大学）
A concurrent global-local numerical method for multiscale parabolic equations
National University of Singapore（新加坡国立大学）；SKLMS, Institute of Computational Mathematics and Scientific/Engineering Computing, AMSS, Chinese Academy of Sciences（中国科学院数学与系统科学研究院计算数学与科学工程计算研究所）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
Bound-Preserving Flux-Corrected Transport Methods for Solving Richards' Equation
Department of Civil and Environmental Engineering, Louisiana State University（路易斯安那州立大学土木与环境工程系）；TU Dortmund University（多特蒙德工业大学）；Center for Computation and Technology, Louisiana State University（路易斯安那州立大学计算与技术中心）
Preconditioning for the high-order sampling of the invariant distribution of parabolic semilinear SPDEs
Universite de Pau et des Pays de l’Adour, E2S UPPA, CNRS, LMAP（波城大学、E2S 巴约讷-比亚里茨大学、法国国家科学研究中心、数学与及其应用实验室）；Univ Rennes, INRIA (MINGuS), IRMAR - UMR 6625, ENS Rennes（雷恩大学、法国国家信息与自动化研究所（MINGuS）、雷恩数学研究所（UMR 6625）、雷恩高等师范学院）；Univ Rennes, CNRS, IRMAR - UMR 6625, ENS Rennes（雷恩大学、法国国家科学研究中心、雷恩数学研究所（UMR 6625）、雷恩高等师范学院）；Section of Mathematics, University of Geneva（日内瓦大学数学系）
Structure-Preserving Quantum Method of Lines for Evolutionary PDEs with Mixed Boundary Conditions
Yau Mathematical Sciences Center, Tsinghua University（清华大学丘成桐数学科学中心）；Qiuzhen College, Tsinghua University（清华大学求真学院）；Institute for Applied Mathematics, Tsinghua University（清华大学应用数学中心）；Beijing Institute of Mathematical Sciences and Applications（北京国际数学研究中心）
