A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition

<div><svg height="12.3328pt" id="M127" style="vertical-align:-3.31848pt" version="1.1" viewbox="-0.0498162 -9.01432 19.0252 12.3328" width="19.0252pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M421 658C372 683 338 687 289 687C174 687 100 624 100 549C100 458 196 404 336 401C341 401 342 386 347 381V379C196 347 110 258 110 154C110 43 211 -15 326 -15C463 -15 584 71 584 161C584 228 542 276 450 276C352 276 275 206 248 98L269 92C293 177 364 234 433 234C485 234 520 207 520 160C520 131 509 102 485 75C454 40 404 15 336 15C244 15 192 73 192 134C192 220 267 311 387 343C403 334 419 329 440 329C464 329 482 338 482 357C482 377 454 385 407 388C404 392 404 393 403 400C588 413 734 506 734 585C734 633 698 658 634 658C511 658 343 568 332 432C229 432 141 472 141 546C141 610 204 657 301 657C356 657 372 656 411 640L421 658ZM693 588C693 524 566 441 403 430C416 521 546 621 641 621C670 621 693 608 693 588Z"></path></g><g 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xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M421 658C372 683 338 687 289 687C174 687 100 624 100 549C100 458 196 404 336 401C341 401 342 386 347 381V379C196 347 110 258 110 154C110 43 211 -15 326 -15C463 -15 584 71 584 161C584 228 542 276 450 276C352 276 275 206 248 98L269 92C293 177 364 234 433 234C485 234 520 207 520 160C520 131 509 102 485 75C454 40 404 15 336 15C244 15 192 73 192 134C192 220 267 311 387 343C403 334 419 329 440 329C464 329 482 338 482 357C482 377 454 385 407 388C404 392 404 393 403 400C588 413 734 506 734 585C734 633 698 658 634 658C511 658 343 568 332 432C229 432 141 472 141 546C141 610 204 657 301 657C356 657 372 656 411 640L421 658ZM693 588C693 524 566 441 403 430C416 521 546 621 641 621C670 621 693 608 693 588Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,9.75,3.132)"><path d="M856 669C824 657 796 643 772 628C709 667 631 687 551 687C314 687 158 548 158 412C158 332 216 282 300 282C445 282 516 426 527 561H507C478 406 414 312 302 312C245 312 199 352 199 408C199 535 345 657 550 657C617 657 687 644 742 609C644 538 583 430 495 271C383 71 317 16 193 16C122 16 73 60 73 95H75C82 84 97 83 105 83C133 83 148 106 148 131C148 160 125 179 97 179C64 179 38 151 38 108C38 42 104 -15 211 -15C368 -15 496 71 610 302C672 426 704 522 770 588C803 559 825 518 825 465C825 382 787 316 731 316C710 316 697 328 697 345C697 358 700 380 733 399L723 417C688 398 665 367 665 338C665 303 688 272 737 272C821 272 886 359 886 447C886 516 850 572 797 612C816 627 836 639 863 650L856 669Z"></path></g></svg> (asterisk) versus the number of modes for <span class="nowrap"><svg height="12.7178pt" id="M129" style="vertical-align:-3.42947pt" version="1.1" viewbox="-0.0498162 -9.28833 40.8257 12.7178" width="40.8257pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M619 670C619 686 593 712 555 712S459 686 410 634S335 504 320 430H250L219 400L222 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644 742 609C644 538 583 430 495 271C383 71 317 16 193 16C122 16 73 60 73 95H75C82 84 97 83 105 83C133 83 148 106 148 131C148 160 125 179 97 179C64 179 38 151 38 108C38 42 104 -15 211 -15C368 -15 496 71 610 302C672 426 704 522 770 588C803 559 825 518 825 465C825 382 787 316 731 316C710 316 697 328 697 345C697 358 700 380 733 399L723 417C688 398 665 367 665 338C665 303 688 272 737 272C821 272 886 359 886 447C886 516 850 572 797 612C816 627 836 639 863 650L856 669Z"></path></g><g transform="matrix(.013,0,0,-0.013,21.702,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,32.965,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,39.205,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 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transform="matrix(.013,0,0,-0.013,39.673,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,45.913,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,52.153,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g></svg>.</span> No modal adaptivity.</div>

Complexity

fig3

Figure 3

Figure 3: A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition 