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f | 1 | { | f | 1 | { |
2 | "author": "Carle, Constantin", | 2 | "author": "Carle, Constantin", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
4 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 4 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
5 | "doi": "10.35097/1456", | 5 | "doi": "10.35097/1456", | ||
6 | "doi_date_published": "2023", | 6 | "doi_date_published": "2023", | ||
7 | "doi_publisher": "", | 7 | "doi_publisher": "", | ||
8 | "doi_status": "True", | 8 | "doi_status": "True", | ||
9 | "groups": [], | 9 | "groups": [], | ||
10 | "id": "7a61c416-4df9-478d-b01d-a401385629b5", | 10 | "id": "7a61c416-4df9-478d-b01d-a401385629b5", | ||
11 | "isopen": false, | 11 | "isopen": false, | ||
12 | "license_id": "CC BY-NC-SA 4.0 | 12 | "license_id": "CC BY-NC-SA 4.0 | ||
13 | Attribution-NonCommercial-ShareAlike", | 13 | Attribution-NonCommercial-ShareAlike", | ||
14 | "license_title": "CC BY-NC-SA 4.0 | 14 | "license_title": "CC BY-NC-SA 4.0 | ||
15 | Attribution-NonCommercial-ShareAlike", | 15 | Attribution-NonCommercial-ShareAlike", | ||
16 | "metadata_created": "2023-08-04T08:50:49.682034", | 16 | "metadata_created": "2023-08-04T08:50:49.682034", | ||
t | 17 | "metadata_modified": "2023-08-04T08:50:49.682039", | t | 17 | "metadata_modified": "2023-08-04T08:52:04.962812", |
18 | "name": "rdr-doi-10-35097-1456", | 18 | "name": "rdr-doi-10-35097-1456", | ||
19 | "notes": "Abstract: This code was used for the numerical experiments | 19 | "notes": "Abstract: This code was used for the numerical experiments | ||
20 | in the CRC 1173 Preprint 2023/2 (URL: | 20 | in the CRC 1173 Preprint 2023/2 (URL: | ||
21 | https://www.waves.kit.edu/downloads/CRC1173_Preprint_2023-2.pdf) by C. | 21 | https://www.waves.kit.edu/downloads/CRC1173_Preprint_2023-2.pdf) by C. | ||
22 | Carle and M. Hochbruck.\r\nTechnicalRemarks: The scripts are intended | 22 | Carle and M. Hochbruck.\r\nTechnicalRemarks: The scripts are intended | ||
23 | to reproduce the figures from the preprint \r\n\r\n\"Error analysis of | 23 | to reproduce the figures from the preprint \r\n\r\n\"Error analysis of | ||
24 | second-order local time integration methods \r\n for | 24 | second-order local time integration methods \r\n for | ||
25 | discontinuous Galerkin discretizations of linear wave | 25 | discontinuous Galerkin discretizations of linear wave | ||
26 | equations\"\r\n\r\nby Constantin Carle and Marlis | 26 | equations\"\r\n\r\nby Constantin Carle and Marlis | ||
27 | ##############################################################\r\n#### | 27 | ##############################################################\r\n#### | ||
28 | Requirements\r\n\r\nThe codes are tested with \r\n\r\nUbuntu 20.04.2 | 28 | Requirements\r\n\r\nThe codes are tested with \r\n\r\nUbuntu 20.04.2 | ||
29 | LTS and Python 3.8.10 and the following version of its | 29 | LTS and Python 3.8.10 and the following version of its | ||
30 | modules:\r\n\r\nnumpy - 1.17.4\r\nscipy \r\n\r\ndolfin | 30 | modules:\r\n\r\nnumpy - 1.17.4\r\nscipy \r\n\r\ndolfin | ||
31 | (fenics) - 2019.2.0.dev0 (including mshr)\r\n\r\nmatplotlib - | 31 | (fenics) - 2019.2.0.dev0 (including mshr)\r\n\r\nmatplotlib - | ||
32 | 3.1.2\r\ntikzplotlib - 0.9.6\r\npandas - | 32 | 3.1.2\r\ntikzplotlib - 0.9.6\r\npandas - | ||
33 | ##############################################################\r\n#### | 33 | ##############################################################\r\n#### | ||
34 | Generation of figures\r\n\r\nFor the generation of Figure Y, Y=1,2,3, | 34 | Generation of figures\r\n\r\nFor the generation of Figure Y, Y=1,2,3, | ||
35 | either execute the Python script 'script_figY.py' \r\nin a suitable | 35 | either execute the Python script 'script_figY.py' \r\nin a suitable | ||
36 | integrated development environment or run the command\r\n python3 | 36 | integrated development environment or run the command\r\n python3 | ||
37 | script_figY.py\r\nin a terminal session.\r\n\r\nFor the generation of | 37 | script_figY.py\r\nin a terminal session.\r\n\r\nFor the generation of | ||
38 | Figure 4 first execute the Python script 'script_fig4_data.py' | 38 | Figure 4 first execute the Python script 'script_fig4_data.py' | ||
39 | \r\n(via a suitable integrated development environment or in a | 39 | \r\n(via a suitable integrated development environment or in a | ||
40 | terminal session)\r\nwhich generates and saves the data for the | 40 | terminal session)\r\nwhich generates and saves the data for the | ||
41 | figure.\r\nTo obtain the figure execute the script | 41 | figure.\r\nTo obtain the figure execute the script | ||
42 | 'script_fig4_plot.py'\r\n(the script 'script_fig4_plot_single.py' | 42 | 'script_fig4_plot.py'\r\n(the script 'script_fig4_plot_single.py' | ||
43 | generates a different view of the data). \r\n\r\nAdditionally to | 43 | generates a different view of the data). \r\n\r\nAdditionally to | ||
44 | the plots you can get the tikz-files which were used for | 44 | the plots you can get the tikz-files which were used for | ||
45 | \r\ngenerating the figures in the preprint by setting the Boolean | 45 | \r\ngenerating the figures in the preprint by setting the Boolean | ||
46 | variable 'write_tikz' \r\nfrom 'False' to 'True' at the beginning of | 46 | variable 'write_tikz' \r\nfrom 'False' to 'True' at the beginning of | ||
47 | the scripts.\r\n \r\nThe scripts for Figure 1, 2, 4 run in parallel on | 47 | the scripts.\r\n \r\nThe scripts for Figure 1, 2, 4 run in parallel on | ||
48 | all available processors. \r\nThe number of used processors can be | 48 | all available processors. \r\nThe number of used processors can be | ||
49 | specified with the \r\nvariable 'num_processors_max' (integer). | 49 | specified with the \r\nvariable 'num_processors_max' (integer). | ||
50 | - 1.3.3", | 50 | - 1.3.3", | ||
51 | "num_resources": 0, | 51 | "num_resources": 0, | ||
52 | "num_tags": 9, | 52 | "num_tags": 9, | ||
53 | "orcid": "", | 53 | "orcid": "", | ||
54 | "organization": { | 54 | "organization": { | ||
55 | "approval_status": "approved", | 55 | "approval_status": "approved", | ||
56 | "created": "2023-01-12T13:30:23.238233", | 56 | "created": "2023-01-12T13:30:23.238233", | ||
57 | "description": "RADAR (Research Data Repository) is a | 57 | "description": "RADAR (Research Data Repository) is a | ||
58 | cross-disciplinary repository for archiving and publishing research | 58 | cross-disciplinary repository for archiving and publishing research | ||
59 | data from completed scientific studies and projects. The focus is on | 59 | data from completed scientific studies and projects. The focus is on | ||
60 | research data from subjects that do not yet have their own | 60 | research data from subjects that do not yet have their own | ||
61 | discipline-specific infrastructures for research data management. ", | 61 | discipline-specific infrastructures for research data management. ", | ||
62 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 62 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
63 | "image_url": "radar-logo.svg", | 63 | "image_url": "radar-logo.svg", | ||
64 | "is_organization": true, | 64 | "is_organization": true, | ||
65 | "name": "radar", | 65 | "name": "radar", | ||
66 | "state": "active", | 66 | "state": "active", | ||
67 | "title": "RADAR", | 67 | "title": "RADAR", | ||
68 | "type": "organization" | 68 | "type": "organization" | ||
69 | }, | 69 | }, | ||
70 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 70 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
71 | "private": false, | 71 | "private": false, | ||
72 | "production_year": "2023", | 72 | "production_year": "2023", | ||
73 | "publication_year": "2023", | 73 | "publication_year": "2023", | ||
74 | "publishers": [ | 74 | "publishers": [ | ||
75 | { | 75 | { | ||
76 | "publisher": "Karlsruhe Institute of Technology" | 76 | "publisher": "Karlsruhe Institute of Technology" | ||
77 | } | 77 | } | ||
78 | ], | 78 | ], | ||
79 | "relationships_as_object": [], | 79 | "relationships_as_object": [], | ||
80 | "relationships_as_subject": [], | 80 | "relationships_as_subject": [], | ||
81 | "repository_name": "RADAR (Research Data Repository)", | 81 | "repository_name": "RADAR (Research Data Repository)", | ||
82 | "resources": [], | 82 | "resources": [], | ||
83 | "services_used_list": "", | 83 | "services_used_list": "", | ||
84 | "source_metadata_created": "2023", | 84 | "source_metadata_created": "2023", | ||
85 | "source_metadata_modified": "", | 85 | "source_metadata_modified": "", | ||
86 | "state": "active", | 86 | "state": "active", | ||
87 | "subject_areas": [ | 87 | "subject_areas": [ | ||
88 | { | 88 | { | ||
89 | "subject_area_additional": "", | 89 | "subject_area_additional": "", | ||
90 | "subject_area_name": "Mathematics" | 90 | "subject_area_name": "Mathematics" | ||
91 | } | 91 | } | ||
92 | ], | 92 | ], | ||
93 | "tags": [ | 93 | "tags": [ | ||
94 | { | 94 | { | ||
95 | "display_name": "CFL condition", | 95 | "display_name": "CFL condition", | ||
96 | "id": "1b514a01-be77-4cbd-9128-0e7ef27faafe", | 96 | "id": "1b514a01-be77-4cbd-9128-0e7ef27faafe", | ||
97 | "name": "CFL condition", | 97 | "name": "CFL condition", | ||
98 | "state": "active", | 98 | "state": "active", | ||
99 | "vocabulary_id": null | 99 | "vocabulary_id": null | ||
100 | }, | 100 | }, | ||
101 | { | 101 | { | ||
102 | "display_name": "Chebyshev polynomials", | 102 | "display_name": "Chebyshev polynomials", | ||
103 | "id": "14f029e3-2f69-47ae-83d3-ea5a865e1e1c", | 103 | "id": "14f029e3-2f69-47ae-83d3-ea5a865e1e1c", | ||
104 | "name": "Chebyshev polynomials", | 104 | "name": "Chebyshev polynomials", | ||
105 | "state": "active", | 105 | "state": "active", | ||
106 | "vocabulary_id": null | 106 | "vocabulary_id": null | ||
107 | }, | 107 | }, | ||
108 | { | 108 | { | ||
109 | "display_name": "discontinuous Galerkin method", | 109 | "display_name": "discontinuous Galerkin method", | ||
110 | "id": "3b870e5c-5720-4d70-8261-bb7974bcbf65", | 110 | "id": "3b870e5c-5720-4d70-8261-bb7974bcbf65", | ||
111 | "name": "discontinuous Galerkin method", | 111 | "name": "discontinuous Galerkin method", | ||
112 | "state": "active", | 112 | "state": "active", | ||
113 | "vocabulary_id": null | 113 | "vocabulary_id": null | ||
114 | }, | 114 | }, | ||
115 | { | 115 | { | ||
116 | "display_name": "error analysis", | 116 | "display_name": "error analysis", | ||
117 | "id": "408f9606-823e-4494-9527-e10be495ea6d", | 117 | "id": "408f9606-823e-4494-9527-e10be495ea6d", | ||
118 | "name": "error analysis", | 118 | "name": "error analysis", | ||
119 | "state": "active", | 119 | "state": "active", | ||
120 | "vocabulary_id": null | 120 | "vocabulary_id": null | ||
121 | }, | 121 | }, | ||
122 | { | 122 | { | ||
123 | "display_name": "leapfrog method", | 123 | "display_name": "leapfrog method", | ||
124 | "id": "bd9eba89-751f-4d86-a18f-162fb28d8bcf", | 124 | "id": "bd9eba89-751f-4d86-a18f-162fb28d8bcf", | ||
125 | "name": "leapfrog method", | 125 | "name": "leapfrog method", | ||
126 | "state": "active", | 126 | "state": "active", | ||
127 | "vocabulary_id": null | 127 | "vocabulary_id": null | ||
128 | }, | 128 | }, | ||
129 | { | 129 | { | ||
130 | "display_name": "local time-stepping", | 130 | "display_name": "local time-stepping", | ||
131 | "id": "302a87af-c66f-49dc-8142-8c5e74748420", | 131 | "id": "302a87af-c66f-49dc-8142-8c5e74748420", | ||
132 | "name": "local time-stepping", | 132 | "name": "local time-stepping", | ||
133 | "state": "active", | 133 | "state": "active", | ||
134 | "vocabulary_id": null | 134 | "vocabulary_id": null | ||
135 | }, | 135 | }, | ||
136 | { | 136 | { | ||
137 | "display_name": "locally implicit", | 137 | "display_name": "locally implicit", | ||
138 | "id": "6b602f24-f94d-4718-bb2b-ebb3b3100b81", | 138 | "id": "6b602f24-f94d-4718-bb2b-ebb3b3100b81", | ||
139 | "name": "locally implicit", | 139 | "name": "locally implicit", | ||
140 | "state": "active", | 140 | "state": "active", | ||
141 | "vocabulary_id": null | 141 | "vocabulary_id": null | ||
142 | }, | 142 | }, | ||
143 | { | 143 | { | ||
144 | "display_name": "time integration", | 144 | "display_name": "time integration", | ||
145 | "id": "05c33a87-3032-479c-af53-5a6cb2b6da8d", | 145 | "id": "05c33a87-3032-479c-af53-5a6cb2b6da8d", | ||
146 | "name": "time integration", | 146 | "name": "time integration", | ||
147 | "state": "active", | 147 | "state": "active", | ||
148 | "vocabulary_id": null | 148 | "vocabulary_id": null | ||
149 | }, | 149 | }, | ||
150 | { | 150 | { | ||
151 | "display_name": "wave equation", | 151 | "display_name": "wave equation", | ||
152 | "id": "cec2cefd-f3c3-4de9-b1d5-f680a3a88ff5", | 152 | "id": "cec2cefd-f3c3-4de9-b1d5-f680a3a88ff5", | ||
153 | "name": "wave equation", | 153 | "name": "wave equation", | ||
154 | "state": "active", | 154 | "state": "active", | ||
155 | "vocabulary_id": null | 155 | "vocabulary_id": null | ||
156 | } | 156 | } | ||
157 | ], | 157 | ], | ||
158 | "title": "Numerical experiments to \"error analysis of second-order | 158 | "title": "Numerical experiments to \"error analysis of second-order | ||
159 | local time integration methods for discontinuous galerkin | 159 | local time integration methods for discontinuous galerkin | ||
160 | discretizations of linear wave equations\"", | 160 | discretizations of linear wave equations\"", | ||
161 | "type": "vdataset", | 161 | "type": "vdataset", | ||
162 | "url": "https://doi.org/10.35097/1456" | 162 | "url": "https://doi.org/10.35097/1456" | ||
163 | } | 163 | } |