Improved error estimates of the time-splitting methods for the long-time dynamics of the Klein–Gordon–Dirac system with the small coupling constant

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Jiyong Li
{"title":"Improved error estimates of the time-splitting methods for the long-time dynamics of the Klein–Gordon–Dirac system with the small coupling constant","authors":"Jiyong Li","doi":"10.1002/num.23084","DOIUrl":null,"url":null,"abstract":"We provide improved uniform error estimates for the time-splitting Fourier pseudo-spectral (TSFP) methods applied to the Klein–Gordon–Dirac system (KGDS) with the small parameter <mjx-container ctxtmenu_counter=\"0\" jax=\"CHTML\" style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" data-semantic-complexity=\"5.5\" location=\"graphic/num23084-math-0001.png\"><mjx-semantics data-semantic-complexity=\"5.5\"><mjx-maction data-collapsible=\"true\" data-semantic-complexity=\"1.5\" toggle=\"2\"><mjx-mrow data-semantic-children=\"0,8\" data-semantic-complexity=\"16\" data-semantic-content=\"1\" data-semantic- data-semantic-role=\"element\" data-semantic-type=\"infixop\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"infixop,∈\" data-semantic-parent=\"9\" data-semantic-role=\"element\" data-semantic-type=\"operator\" rspace=\"5\" space=\"5\"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-children=\"7\" data-semantic-complexity=\"11\" data-semantic-content=\"2,6\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"8\" data-semantic-role=\"open\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-children=\"3,4,5\" data-semantic-complexity=\"6\" data-semantic-content=\"4\" data-semantic- data-semantic-parent=\"8\" data-semantic-role=\"sequence\" data-semantic-type=\"punctuated\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"punctuated\" data-semantic-parent=\"7\" data-semantic-role=\"comma\" data-semantic-type=\"punctuation\" rspace=\"3\" style=\"margin-left: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn></mjx-mrow><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"8\" data-semantic-role=\"close\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo></mjx-mrow></mjx-mrow></mjx-maction></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:num:media:num23084:num23084-math-0001\" data-semantic-complexity=\"5.5\" display=\"inline\" location=\"graphic/num23084-math-0001.png\" overflow=\"scroll\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics data-semantic-complexity=\"5.5\"><maction actiontype=\"toggle\" data-collapsible=\"true\" data-semantic-complexity=\"1.5\" selection=\"2\"><mtext mathcolor=\"blue\">◂+▸</mtext><mrow data-semantic-=\"\" data-semantic-children=\"0,8\" data-semantic-complexity=\"16\" data-semantic-content=\"1\" data-semantic-role=\"element\" data-semantic-type=\"infixop\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"9\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\">ε</mi><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"infixop,∈\" data-semantic-parent=\"9\" data-semantic-role=\"element\" data-semantic-type=\"operator\">∈</mo><mrow data-semantic-=\"\" data-semantic-children=\"7\" data-semantic-complexity=\"11\" data-semantic-content=\"2,6\" data-semantic-parent=\"9\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"fenced\" data-semantic-parent=\"8\" data-semantic-role=\"open\" data-semantic-type=\"fence\" stretchy=\"false\">(</mo><mrow data-semantic-=\"\" data-semantic-children=\"3,4,5\" data-semantic-complexity=\"6\" data-semantic-content=\"4\" data-semantic-parent=\"8\" data-semantic-role=\"sequence\" data-semantic-type=\"punctuated\"><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic-parent=\"7\" data-semantic-role=\"integer\" data-semantic-type=\"number\">0</mn><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"punctuated\" data-semantic-parent=\"7\" data-semantic-role=\"comma\" data-semantic-type=\"punctuation\">,</mo><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic-parent=\"7\" data-semantic-role=\"integer\" data-semantic-type=\"number\">1</mn></mrow><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"fenced\" data-semantic-parent=\"8\" data-semantic-role=\"close\" data-semantic-type=\"fence\" stretchy=\"false\">]</mo></mrow></mrow></maction>$$ \\varepsilon \\in \\left(0,1\\right] $$</annotation></semantics></math></mjx-assistive-mml></mjx-container>. We first reformulate the KGDS into a coupled Schrödinger–Dirac system (CSDS) and then apply the second-order Strang splitting method to CSDS with the spatial discretization provided by Fourier pseudo-spectral method. Based on rigorous analysis, we establish improved uniform error bounds for the second-order Strang splitting method at <mjx-container ctxtmenu_counter=\"1\" jax=\"CHTML\" style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" data-semantic-complexity=\"11\" location=\"graphic/num23084-math-0002.png\"><mjx-semantics data-semantic-complexity=\"11\"><mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,17\" data-semantic-complexity=\"7\" data-semantic-content=\"18\" data-semantic- data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"19\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic-added=\"true\" data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"19\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-maction data-collapsible=\"true\" data-semantic-complexity=\"2\" toggle=\"2\"><mjx-mrow data-semantic-children=\"16\" data-semantic-complexity=\"21.8\" data-semantic-content=\"1,13\" data-semantic- data-semantic-parent=\"19\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"17\" data-semantic-role=\"open\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-children=\"7,15\" data-semantic-complexity=\"16.8\" data-semantic-content=\"8\" data-semantic- data-semantic-parent=\"17\" data-semantic-role=\"addition\" data-semantic-type=\"infixop\"><mjx-maction data-collapsible=\"true\" data-semantic-complexity=\"2\" toggle=\"2\"><mjx-msup data-semantic-children=\"2,6\" data-semantic-complexity=\"9.8\" data-semantic- data-semantic-parent=\"16\" data-semantic-role=\"latinletter\" data-semantic-type=\"superscript\"><mjx-mrow data-semantic-complexity=\"1\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mrow data-semantic-children=\"3,5\" data-semantic-complexity=\"6\" data-semantic-content=\"4\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"subtraction\" data-semantic-type=\"infixop\" size=\"s\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"infixop,−\" data-semantic-parent=\"6\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\" rspace=\"1\"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-script></mjx-msup></mjx-maction><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"infixop,+\" data-semantic-parent=\"16\" data-semantic-role=\"addition\" data-semantic-type=\"operator\" rspace=\"4\" space=\"4\"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"9,12\" data-semantic-complexity=\"10.8\" data-semantic-content=\"14\" data-semantic- data-semantic-parent=\"16\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"15\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic-added=\"true\" data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"15\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-msup data-semantic-children=\"10,11\" data-semantic-complexity=\"5.8\" data-semantic- data-semantic-parent=\"15\" data-semantic-role=\"greekletter\" data-semantic-type=\"superscript\"><mjx-mrow data-semantic-complexity=\"1\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"12\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow><mjx-script style=\"vertical-align: 0.363em; margin-left: 0.054em;\"><mjx-mrow data-semantic-complexity=\"1\" size=\"s\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"12\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-script></mjx-msup></mjx-mrow></mjx-mrow><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"17\" data-semantic-role=\"close\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo></mjx-mrow></mjx-maction></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:num:media:num23084:num23084-math-0002\" data-semantic-complexity=\"11\" display=\"inline\" location=\"graphic/num23084-math-0002.png\" overflow=\"scroll\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics data-semantic-complexity=\"11\"><mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,17\" data-semantic-complexity=\"7\" data-semantic-content=\"18\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"19\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\">O</mi><mo data-semantic-=\"\" data-semantic-added=\"true\" data-semantic-complexity=\"1\" data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"19\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\">⁢</mo><maction actiontype=\"toggle\" data-collapsible=\"true\" data-semantic-complexity=\"2\" selection=\"2\"><mtext mathcolor=\"blue\">◂()▸</mtext><mrow data-semantic-=\"\" data-semantic-children=\"16\" data-semantic-complexity=\"21.8\" data-semantic-content=\"1,13\" data-semantic-parent=\"19\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"fenced\" data-semantic-parent=\"17\" data-semantic-role=\"open\" data-semantic-type=\"fence\" stretchy=\"false\">(</mo><mrow data-semantic-=\"\" data-semantic-children=\"7,15\" data-semantic-complexity=\"16.8\" data-semantic-content=\"8\" data-semantic-parent=\"17\" data-semantic-role=\"addition\" data-semantic-type=\"infixop\"><maction actiontype=\"toggle\" data-collapsible=\"true\" data-semantic-complexity=\"2\" selection=\"2\"><mtext mathcolor=\"blue\">◂◽˙▸</mtext><msup data-semantic-=\"\" data-semantic-children=\"2,6\" data-semantic-complexity=\"9.8\" data-semantic-parent=\"16\" data-semantic-role=\"latinletter\" data-semantic-type=\"superscript\"><mrow data-semantic-complexity=\"1\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"7\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\">h</mi></mrow><mrow data-semantic-=\"\" data-semantic-children=\"3,5\" data-semantic-complexity=\"6\" data-semantic-content=\"4\" data-semantic-parent=\"7\" data-semantic-role=\"subtraction\" data-semantic-type=\"infixop\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"6\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\">m</mi><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"infixop,−\" data-semantic-parent=\"6\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\" form=\"prefix\">−</mo><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic-parent=\"6\" data-semantic-role=\"integer\" data-semantic-type=\"number\">1</mn></mrow></msup></maction><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"infixop,+\" data-semantic-parent=\"16\" data-semantic-role=\"addition\" data-semantic-type=\"operator\">+</mo><mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"9,12\" data-semantic-complexity=\"10.8\" data-semantic-content=\"14\" data-semantic-parent=\"16\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"15\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\">ε</mi><mo data-semantic-=\"\" data-semantic-added=\"true\" data-semantic-complexity=\"1\" data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"15\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\">⁢</mo><msup data-semantic-=\"\" data-semantic-children=\"10,11\" data-semantic-complexity=\"5.8\" data-semantic-parent=\"15\" data-semantic-role=\"greekletter\" data-semantic-type=\"superscript\"><mrow data-semantic-complexity=\"1\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"12\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\">τ</mi></mrow><mrow data-semantic-complexity=\"1\"><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic-parent=\"12\" data-semantic-role=\"integer\" data-semantic-type=\"number\">2</mn></mrow></msup></mrow></mrow><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"fenced\" data-semantic-parent=\"17\" data-semantic-role=\"close\" data-semantic-type=\"fence\" stretchy=\"false\">)</mo></mrow></maction></mrow>$$ O\\left({h}^{m-1}+\\varepsilon {\\tau}^2\\right) $$</annotation></semantics></math></mjx-assistive-mml></mjx-container> up to the long time at <mjx-container ctxtmenu_counter=\"2\" jax=\"CHTML\" style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" data-semantic-complexity=\"5.5\" location=\"graphic/num23084-math-0003.png\"><mjx-semantics data-semantic-complexity=\"5.5\"><mjx-maction data-collapsible=\"true\" data-semantic-complexity=\"1.5\" toggle=\"2\"><mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,7\" data-semantic-complexity=\"16\" data-semantic-content=\"8\" data-semantic- data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic-added=\"true\" data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"9\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-children=\"6\" data-semantic-complexity=\"11\" data-semantic-content=\"1,5\" data-semantic- data-semantic-parent=\"9\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"7\" data-semantic-role=\"open\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-children=\"2,4\" data-semantic-complexity=\"6\" data-semantic-content=\"3\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"division\" data-semantic-type=\"infixop\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"infixop,/\" data-semantic-parent=\"6\" data-semantic-role=\"division\" data-semantic-type=\"operator\" rspace=\"1\" space=\"1\"><mjx-c></mjx-c></mjx-mo><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow><mjx-mo data-semantic-complexity=\"1\" data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"7\" data-semantic-role=\"close\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo></mjx-mrow></mjx-mrow></mjx-maction></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:num:media:num23084:num23084-math-0003\" data-semantic-complexity=\"5.5\" display=\"inline\" location=\"graphic/num23084-math-0003.png\" overflow=\"scroll\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics data-semantic-complexity=\"5.5\"><maction actiontype=\"toggle\" data-collapsible=\"true\" data-semantic-complexity=\"1.5\" selection=\"2\"><mtext mathcolor=\"blue\">◂⋅▸</mtext><mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,7\" data-semantic-complexity=\"16\" data-semantic-content=\"8\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"9\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\">O</mi><mo data-semantic-=\"\" data-semantic-added=\"true\" data-semantic-complexity=\"1\" data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"9\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\">⁢</mo><mrow data-semantic-=\"\" data-semantic-children=\"6\" data-semantic-complexity=\"11\" data-semantic-content=\"1,5\" data-semantic-parent=\"9\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"fenced\" data-semantic-parent=\"7\" data-semantic-role=\"open\" data-semantic-type=\"fence\" stretchy=\"false\">(</mo><mrow data-semantic-=\"\" data-semantic-children=\"2,4\" data-semantic-complexity=\"6\" data-semantic-content=\"3\" data-semantic-parent=\"7\" data-semantic-role=\"division\" data-semantic-type=\"infixop\"><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"normal\" data-semantic-parent=\"6\" data-semantic-role=\"integer\" data-semantic-type=\"number\">1</mn><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"infixop,/\" data-semantic-parent=\"6\" data-semantic-role=\"division\" data-semantic-type=\"operator\" stretchy=\"false\">/</mo><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-complexity=\"1\" data-semantic-font=\"italic\" data-semantic-parent=\"6\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\">ε</mi></mrow><mo data-semantic-=\"\" data-semantic-complexity=\"1\" data-semantic-operator=\"fenced\" data-semantic-parent=\"7\" data-semantic-role=\"close\" data-semantic-type=\"fence\" stretchy=\"false\">)</mo></mrow></mrow></maction>$$ O\\left(1/\\varepsilon \\right) $$</annotation></semantics></math></mjx-assistive-mml></mjx-container>. In addition to the conventional analysis methods, we mainly apply the regularity compensation oscillation technique for the analysis of long time dynamic simulation. The numerical results show that our method and conclusion are not only suitable for one-dimensional problem, but also can be directly extended to higher dimensional problem and highly oscillatory problem. As far as we know there has not been any relevant long time analysis and any improved uniform error bounds for the TSFP method solving the KGDS. Our methods are novel and provides a reference for analyzing the improved error bounds of other coupled systems similar to the KGDS.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/num.23084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0

Abstract

We provide improved uniform error estimates for the time-splitting Fourier pseudo-spectral (TSFP) methods applied to the Klein–Gordon–Dirac system (KGDS) with the small parameter ◂+▸ε(0,1]$$ \varepsilon \in \left(0,1\right] $$. We first reformulate the KGDS into a coupled Schrödinger–Dirac system (CSDS) and then apply the second-order Strang splitting method to CSDS with the spatial discretization provided by Fourier pseudo-spectral method. Based on rigorous analysis, we establish improved uniform error bounds for the second-order Strang splitting method at O◂()▸(◂◽˙▸hm1+ετ2)$$ O\left({h}^{m-1}+\varepsilon {\tau}^2\right) $$ up to the long time at ◂⋅▸O(1/ε)$$ O\left(1/\varepsilon \right) $$. In addition to the conventional analysis methods, we mainly apply the regularity compensation oscillation technique for the analysis of long time dynamic simulation. The numerical results show that our method and conclusion are not only suitable for one-dimensional problem, but also can be directly extended to higher dimensional problem and highly oscillatory problem. As far as we know there has not been any relevant long time analysis and any improved uniform error bounds for the TSFP method solving the KGDS. Our methods are novel and provides a reference for analyzing the improved error bounds of other coupled systems similar to the KGDS.
具有小耦合常数的克莱因-戈登-狄拉克系统长时动力学时间分割方法的改进误差估计
我们为应用于具有小参数◂+▸ε∈(0,1]$$ \varepsilon \in \left(0,1\right] $$$的Klein-Gordon-Dirac系统(KGDS)的时间分裂傅立叶伪谱(TSFP)方法提供了改进的均匀误差估计。我们首先将 KGDS 重述为耦合薛定谔-狄拉克系统(CSDS),然后将二阶斯特朗分裂法应用于 CSDS,并用傅立叶伪谱法提供空间离散化。基于严格的分析、我们建立了二阶斯特朗分裂法在 O◂()▸(◂◽˙▸hm-1+ετ2)$$ O\left({h}^{m-1}+\varepsilon {\tau}^2\right) $$ 直到 ◂⋅▸O(1/ε)$$ O\left(1/\varepsilon \right) $$ 的长时。除常规分析方法外,我们主要应用正则补偿振荡技术进行长时间动态模拟分析。数值结果表明,我们的方法和结论不仅适用于一维问题,而且可以直接扩展到高维问题和高振荡问题。据我们所知,目前还没有针对求解 KGDS 的 TSFP 方法的相关长时间分析和改进的均匀误差边界。我们的方法很新颖,为分析类似 KGDS 的其他耦合系统的改进误差边界提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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