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Diffusion equation

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is used to rewrite the anisotropic tensor diffusion equation, in standard discretization schemes, because direct discretization of the diffusion equation with only first order spatial central differences leads to checkerboard artifacts. The rewritten diffusion equation used in image filtering:
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The diffusion equation is continuous in both space and time. One may discretize space, time, or both space and time, which arise in application. Discretizing time alone just corresponds to taking time slices of the continuous system, and no new phenomena arise. In discretizing space alone, the
493: 1041: 603: 198: 1117: 781: 698: 836: 293: 640:, which states that a change in density in any part of the system is due to inflow and outflow of material into and out of that part of the system. Effectively, no material is created or destroyed: 515: 1127: 711: 643: 77: 1376: 488:{\displaystyle {\frac {\partial \phi (\mathbf {r} ,t)}{\partial t}}=\sum _{i=1}^{3}\sum _{j=1}^{3}{\frac {\partial }{\partial x_{i}}}\left} 704:
is the flux of the diffusing material. The diffusion equation can be obtained easily from this when combined with the phenomenological
1036:{\displaystyle {\frac {\partial \phi (\mathbf {r} ,t)}{\partial t}}=\nabla \cdot \left\nabla \phi (\mathbf {r} ,t)+{\rm {tr}}{\Big }} 1371: 24: 708:, which states that the flux of the diffusing material in any part of the system is proportional to the local density gradient: 1381: 1337: 1287: 56: 272:. If the diffusion coefficient depends on the density then the equation is nonlinear, otherwise it is linear. 1351: 506: 1112: 1107: 32: 1102: 786: 621: 598:{\displaystyle {\frac {\partial \phi (\mathbf {r} ,t)}{\partial t}}=D\nabla ^{2}\phi (\mathbf {r} ,t),} 809: 798: 284: 1242: 1075:. The spatial derivatives can then be approximated by two first order and a second order central 1346: 1044: 266: 248: 1202: 1122: 1118:
Radiative transfer equation and diffusion theory for photon transport in biological tissue
193:{\displaystyle {\frac {\partial \phi (\mathbf {r} ,t)}{\partial t}}=\nabla \cdot {\big },} 8: 1092: 805: 637: 1206: 1366: 1159: 1148:"Advanced Analytic Self-Similar Solutions of Regular and Irregular Diffusion Equations" 48: 1283: 1220: 1076: 44: 1334: 1210: 1169: 1072: 16:
Equation that describes density changes of a material that is diffusing in a medium
776:{\displaystyle \mathbf {j} =-D(\phi ,\mathbf {r} )\,\nabla \phi (\mathbf {r} ,t).} 1341: 813: 693:{\displaystyle {\frac {\partial \phi }{\partial t}}+\nabla \cdot \mathbf {j} =0,} 28: 36: 27:. In physics, it describes the macroscopic behavior of many micro-particles in 1360: 1224: 1215: 1190: 1097: 705: 609: 60: 1068: 829: 31:, resulting from the random movements and collisions of the particles (see 1174: 1147: 1083:
with a varying kernel (stencil) of size 3 × 3 in 2D and 3 × 3 × 3 in 3D.
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A tutorial on the theory behind and solution of the Diffusion Equation.
625: 52: 1052: 287:, and the equation is written (for three dimensional diffusion) as: 1164: 276: 219: 1352:
Classical and nanoscale diffusion (with figures and animations)
1079:. The resulting diffusion algorithm can be written as an image 1048: 275:
The equation above applies when the diffusion coefficient is
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Diffusion Calculator for Impurities & Dopants in Silicon
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The diffusion equation can be trivially derived from the
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The diffusion equation has numerous analytic solutions.
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is constant, then the equation reduces to the following
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The Diffusion Handbook: Applied Solutions for Engineers
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Numerical solution of the convection–diffusion equation
816:. In discretizing both time and space, one obtains the 839: 823: 714: 646: 518: 296: 80: 59:
when bulk velocity is zero. It is equivalent to the
1243:"Heroes and Highlights in the History of Diffusion" 1035: 775: 692: 597: 487: 192: 55:. The diffusion equation is a special case of the 1028: 954: 1358: 1303:. Long Island, NY, USA: Dover Publication Inc 1240: 1067:) are symmetric matrices constructed from the 1021: 981: 182: 129: 43:, and applied in many other fields, such as 1145: 1257:Carslaw, H. S. and Jaeger, J. C. (1959). 1214: 1173: 1163: 785:If drift must be taken into account, the 746: 1377:Parabolic partial differential equations 1278:Mathews, Jon; Walker, Robert L. (1970). 789:provides an appropriate generalization. 279:; in the case of anisotropic diffusion, 1182: 25:parabolic partial differential equation 1359: 1320:Gillespie, D.T.; Seitaridou, E (2013) 222:of the diffusing material at location 1308:Transport by Advection and Diffusion. 1282:(2nd ed.), New York: W. A. Benjamin, 1188: 615: 71:The equation is usually written as: 35:). In mathematics, it is related to 13: 1234: 990: 986: 947: 944: 916: 880: 868: 843: 824:Discretization in image processing 747: 670: 658: 650: 563: 547: 522: 464: 439: 385: 381: 325: 300: 157: 121: 109: 84: 14: 1393: 1328: 792: 1241:Mehrer, H.; Stolwijk, A (2009). 1146:Barna, I.F.; MĂĄtyĂĄs, L. (2022). 1006: 972: 926: 904: 853: 757: 739: 716: 677: 579: 532: 449: 429: 310: 167: 147: 94: 1292:Thambynayagam, R. K. M (2011). 1280:Mathematical methods of physics 1372:Partial differential equations 1139: 1055:, in which in image filtering 1051:, and superscript "T" denotes 1016: 1002: 976: 962: 936: 922: 908: 894: 863: 849: 767: 753: 743: 729: 589: 575: 542: 528: 459: 445: 433: 419: 320: 306: 177: 163: 151: 137: 104: 90: 1: 1195:Annalen der Physik und Chemie 1133: 812:, rather than the continuous 631: 57:convection–diffusion equation 1273:The Mathematics of Diffusion 1259:Conduction of Heat in Solids 507:linear differential equation 66: 7: 1382:Functions of space and time 1268:Berlin/Heidelberg: Springer 1086: 622:particle diffusion equation 10: 1400: 796: 624:was originally derived by 608:which is identical to the 63:under some circumstances. 1324:. Oxford University Press 1322:Simple Brownian Diffusion 1275:. Oxford: Clarendon Press 1216:10.1002/andp.18551700105 1108:Fick's laws of diffusion 810:discrete Gaussian kernel 799:Discrete Gaussian kernel 285:positive definite matrix 33:Fick's laws of diffusion 1261:Oxford: Clarendon Press 1113:Maxwell–Stefan equation 1043:where "tr" denotes the 1306:Bennett, T.D: (2013) 1247:Diffusion fundamentals 1103:Fokker–Planck equation 1037: 787:Fokker–Planck equation 777: 694: 599: 489: 378: 357: 265:represents the vector 194: 1310:John Wiley & Sons 1038: 778: 695: 600: 490: 358: 337: 267:differential operator 249:diffusion coefficient 195: 1264:Jacobs. M.H. (1935) 1189:Fick, Adolf (1855). 1175:10.3390/math10183281 1123:Streamline diffusion 837: 712: 644: 516: 294: 78: 1315:Adventure Diffusion 1301:Diffusion Phenomena 1266:Diffusion Processes 1207:1855AnP...170...59F 1093:Continuity equation 638:continuity equation 1340:2009-05-02 at the 1271:Crank, J. (1956). 1077:finite differences 1033: 773: 690: 595: 485: 247:is the collective 190: 49:information theory 21:diffusion equation 1191:"Ueber Diffusion" 1073:structure tensors 996: 875: 665: 616:Historical origin 554: 478: 399: 332: 156: 116: 45:materials science 1389: 1313:Vogel, G (2019) 1254: 1229: 1228: 1218: 1186: 1180: 1179: 1177: 1167: 1143: 1047:of the 2nd rank 1042: 1040: 1039: 1034: 1032: 1031: 1025: 1024: 1009: 998: 997: 994: 985: 984: 975: 958: 957: 951: 950: 929: 915: 911: 907: 876: 874: 866: 856: 841: 806:Green's function 782: 780: 779: 774: 760: 742: 719: 706:Fick's first law 699: 697: 696: 691: 680: 666: 664: 656: 648: 604: 602: 601: 596: 582: 571: 570: 555: 553: 545: 535: 520: 504: 494: 492: 491: 486: 484: 480: 479: 477: 476: 475: 462: 452: 437: 432: 418: 417: 400: 398: 397: 396: 380: 377: 372: 356: 351: 333: 331: 323: 313: 298: 282: 264: 260: 254: 246: 231: 227: 217: 199: 197: 196: 191: 186: 185: 170: 154: 150: 133: 132: 117: 115: 107: 97: 82: 37:Markov processes 1399: 1398: 1392: 1391: 1390: 1388: 1387: 1386: 1357: 1356: 1342:Wayback Machine 1331: 1299:Ghez, R (2001) 1237: 1235:Further reading 1232: 1187: 1183: 1144: 1140: 1136: 1089: 1027: 1026: 1020: 1019: 1005: 993: 989: 980: 979: 971: 953: 952: 943: 942: 925: 903: 890: 886: 867: 852: 842: 840: 838: 835: 834: 826: 814:Gaussian kernel 801: 795: 756: 738: 715: 713: 710: 709: 676: 657: 649: 647: 645: 642: 641: 634: 618: 578: 566: 562: 546: 531: 521: 519: 517: 514: 513: 502: 496: 471: 467: 463: 448: 438: 436: 428: 410: 406: 405: 401: 392: 388: 384: 379: 373: 362: 352: 341: 324: 309: 299: 297: 295: 292: 291: 283:is a symmetric 280: 262: 256: 252: 233: 229: 223: 204: 201: 181: 180: 166: 146: 128: 127: 108: 93: 83: 81: 79: 76: 75: 69: 29:Brownian motion 17: 12: 11: 5: 1397: 1396: 1385: 1384: 1379: 1374: 1369: 1355: 1354: 1349: 1344: 1330: 1329:External links 1327: 1326: 1325: 1318: 1311: 1304: 1297: 1296:. McGraw-Hill 1290: 1276: 1269: 1262: 1255: 1236: 1233: 1231: 1230: 1181: 1137: 1135: 1132: 1131: 1130: 1125: 1120: 1115: 1110: 1105: 1100: 1095: 1088: 1085: 1030: 1023: 1018: 1015: 1012: 1008: 1004: 1001: 992: 988: 983: 978: 974: 970: 967: 964: 961: 956: 949: 946: 941: 938: 935: 932: 928: 924: 921: 918: 914: 910: 906: 902: 899: 896: 893: 889: 885: 882: 879: 873: 870: 865: 862: 859: 855: 851: 848: 845: 825: 822: 794: 793:Discretization 791: 772: 769: 766: 763: 759: 755: 752: 749: 745: 741: 737: 734: 731: 728: 725: 722: 718: 689: 686: 683: 679: 675: 672: 669: 663: 660: 655: 652: 633: 630: 617: 614: 606: 605: 594: 591: 588: 585: 581: 577: 574: 569: 565: 561: 558: 552: 549: 544: 541: 538: 534: 530: 527: 524: 483: 474: 470: 466: 461: 458: 455: 451: 447: 444: 441: 435: 431: 427: 424: 421: 416: 413: 409: 404: 395: 391: 387: 383: 376: 371: 368: 365: 361: 355: 350: 347: 344: 340: 336: 330: 327: 322: 319: 316: 312: 308: 305: 302: 289: 189: 184: 179: 176: 173: 169: 165: 162: 159: 153: 149: 145: 142: 139: 136: 131: 126: 123: 120: 114: 111: 106: 103: 100: 96: 92: 89: 86: 73: 68: 65: 15: 9: 6: 4: 3: 2: 1395: 1394: 1383: 1380: 1378: 1375: 1373: 1370: 1368: 1365: 1364: 1362: 1353: 1350: 1348: 1345: 1343: 1339: 1336: 1333: 1332: 1323: 1319: 1316: 1312: 1309: 1305: 1302: 1298: 1295: 1291: 1289: 1288:0-8053-7002-1 1285: 1281: 1277: 1274: 1270: 1267: 1263: 1260: 1256: 1252: 1248: 1244: 1239: 1238: 1226: 1222: 1217: 1212: 1208: 1204: 1200: 1196: 1192: 1185: 1176: 1171: 1166: 1161: 1157: 1153: 1149: 1142: 1138: 1129: 1126: 1124: 1121: 1119: 1116: 1114: 1111: 1109: 1106: 1104: 1101: 1099: 1098:Heat equation 1096: 1094: 1091: 1090: 1084: 1082: 1078: 1074: 1071:of the image 1070: 1066: 1062: 1058: 1054: 1050: 1046: 1013: 1010: 999: 968: 965: 959: 939: 933: 930: 919: 912: 900: 897: 891: 887: 883: 877: 871: 860: 857: 846: 831: 821: 819: 815: 811: 807: 800: 790: 788: 783: 770: 764: 761: 750: 735: 732: 726: 723: 720: 707: 703: 687: 684: 681: 673: 667: 661: 653: 639: 629: 627: 623: 613: 611: 610:heat equation 592: 586: 583: 572: 567: 559: 556: 550: 539: 536: 525: 512: 511: 510: 508: 499: 495: 481: 472: 468: 456: 453: 442: 425: 422: 414: 411: 407: 402: 393: 389: 374: 369: 366: 363: 359: 353: 348: 345: 342: 338: 334: 328: 317: 314: 303: 288: 286: 278: 273: 271: 268: 259: 250: 244: 240: 236: 226: 221: 215: 211: 207: 200: 187: 174: 171: 160: 143: 140: 134: 124: 118: 112: 101: 98: 87: 72: 64: 62: 61:heat equation 58: 54: 50: 46: 42: 38: 34: 30: 26: 22: 1321: 1314: 1307: 1300: 1293: 1279: 1272: 1265: 1258: 1250: 1246: 1201:(1): 59–86. 1198: 1194: 1184: 1158:(18): 3281. 1155: 1151: 1141: 1069:eigenvectors 1064: 1060: 1056: 830:product rule 827: 808:becomes the 802: 784: 701: 635: 619: 607: 500: 497: 290: 274: 257: 255:at location 251:for density 242: 238: 234: 224: 213: 209: 205: 202: 74: 70: 41:random walks 20: 18: 1152:Mathematics 1081:convolution 818:random walk 1361:Categories 1165:2204.04895 1134:References 797:See also: 632:Derivation 626:Adolf Fick 53:biophysics 39:, such as 1367:Diffusion 1225:0003-3804 1053:transpose 1000:ϕ 991:∇ 987:∇ 966:ϕ 920:ϕ 917:∇ 898:ϕ 884:⋅ 881:∇ 869:∂ 847:ϕ 844:∂ 751:ϕ 748:∇ 733:ϕ 724:− 674:⋅ 671:∇ 659:∂ 654:ϕ 651:∂ 628:in 1855. 573:ϕ 564:∇ 548:∂ 526:ϕ 523:∂ 465:∂ 443:ϕ 440:∂ 423:ϕ 386:∂ 382:∂ 360:∑ 339:∑ 326:∂ 304:ϕ 301:∂ 277:isotropic 228:and time 161:ϕ 158:∇ 141:ϕ 125:⋅ 122:∇ 110:∂ 88:ϕ 85:∂ 67:Statement 1338:Archived 1317:Springer 1087:See also 1253:: 1–32. 1203:Bibcode 220:density 218:is the 1286:  1223:  1049:tensor 700:where 261:; and 203:where 155:  51:, and 1160:arXiv 1045:trace 23:is a 1284:ISBN 1221:ISSN 828:The 620:The 232:and 19:The 1211:doi 1199:170 1170:doi 501:If 270:del 1363:: 1251:11 1249:. 1245:. 1219:. 1209:. 1197:. 1193:. 1168:. 1156:10 1154:. 1150:. 1063:, 820:. 612:. 509:: 241:, 212:, 47:, 1227:. 1213:: 1205:: 1178:. 1172:: 1162:: 1065:r 1061:ϕ 1059:( 1057:D 1029:] 1022:) 1017:) 1014:t 1011:, 1007:r 1003:( 995:T 982:( 977:) 973:r 969:, 963:( 960:D 955:[ 948:r 945:t 940:+ 937:) 934:t 931:, 927:r 923:( 913:] 909:) 905:r 901:, 895:( 892:D 888:[ 878:= 872:t 864:) 861:t 858:, 854:r 850:( 771:. 768:) 765:t 762:, 758:r 754:( 744:) 740:r 736:, 730:( 727:D 721:= 717:j 702:j 688:, 685:0 682:= 678:j 668:+ 662:t 593:, 590:) 587:t 584:, 580:r 576:( 568:2 560:D 557:= 551:t 543:) 540:t 537:, 533:r 529:( 503:D 482:] 473:j 469:x 460:) 457:t 454:, 450:r 446:( 434:) 430:r 426:, 420:( 415:j 412:i 408:D 403:[ 394:i 390:x 375:3 370:1 367:= 364:j 354:3 349:1 346:= 343:i 335:= 329:t 321:) 318:t 315:, 311:r 307:( 281:D 263:∇ 258:r 253:ϕ 245:) 243:r 239:ϕ 237:( 235:D 230:t 225:r 216:) 214:t 210:r 208:( 206:ϕ 188:, 183:] 178:) 175:t 172:, 168:r 164:( 152:) 148:r 144:, 138:( 135:D 130:[ 119:= 113:t 105:) 102:t 99:, 95:r 91:(

Index

parabolic partial differential equation
Brownian motion
Fick's laws of diffusion
Markov processes
random walks
materials science
information theory
biophysics
convection–diffusion equation
heat equation
density
diffusion coefficient
differential operator
del
isotropic
positive definite matrix
linear differential equation
heat equation
particle diffusion equation
Adolf Fick
continuity equation
Fick's first law
Fokker–Planck equation
Discrete Gaussian kernel
Green's function
discrete Gaussian kernel
Gaussian kernel
random walk
product rule
trace

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