A computationally efficient parallel Levenberg‐Marquardt algorithm for highly parameterized inverse model analyses
Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, comp...
Uloženo v:
| Vydáno v: | Water resources research Ročník 52; číslo 9; s. 6948 - 6977 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Washington
John Wiley & Sons, Inc
01.09.2016
American Geophysical Union (AGU) |
| Témata: | |
| ISSN: | 0043-1397, 1944-7973 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg‐Marquardt method for solving inverse modeling problems with a highly parameterized model space. Levenberg‐Marquardt methods require the solution of a linear system of equations which can be prohibitively expensive to compute for moderate to large‐scale problems. Our novel method projects the original linear problem down to a Krylov subspace such that the dimensionality of the problem can be significantly reduced. Furthermore, we store the Krylov subspace computed when using the first damping parameter and recycle the subspace for the subsequent damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved using these computational techniques. We apply this new inverse modeling method to invert for random transmissivity fields in 2‐D and a random hydraulic conductivity field in 3‐D. Our algorithm is fast enough to solve for the distributed model parameters (transmissivity) in the model domain. The algorithm is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). By comparing with Levenberg‐Marquardt methods using standard linear inversion techniques such as QR or SVD methods, our Levenberg‐Marquardt method yields a speed‐up ratio on the order of
∼101 to
∼102 in a multicore computational environment. Therefore, our new inverse modeling method is a powerful tool for characterizing subsurface heterogeneity for moderate to large‐scale problems.
Key Points
We generate a Krylov subspace and obtain a much smaller approximated problem by projecting the original problem down to the subspace
We employ both coarse and fine‐grained parallelism to our LM method to parallelize the implementation of the LM algorithm in two levels
We employ a subspace recycling technique to take the advantage of the solution space previously generated |
|---|---|
| AbstractList | Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg-Marquardt method for solving inverse modeling problems with a highly parameterized model space. Levenberg-Marquardt methods require the solution of a linear system of equations which can be prohibitively expensive to compute for moderate to large-scale problems. Our novel method projects the original linear problem down to a Krylov subspace such that the dimensionality of the problem can be significantly reduced. Furthermore, we store the Krylov subspace computed when using the first damping parameter and recycle the subspace for the subsequent damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved using these computational techniques. We apply this new inverse modeling method to invert for random transmissivity fields in 2-D and a random hydraulic conductivity field in 3-D. Our algorithm is fast enough to solve for the distributed model parameters (transmissivity) in the model domain. The algorithm is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). By comparing with Levenberg-Marquardt methods using standard linear inversion techniques such as QR or SVD methods, our Levenberg-Marquardt method yields a speed-up ratio on the order of 10 1 to 10 2 in a multicore computational environment. Therefore, our new inverse modeling method is a powerful tool for characterizing subsurface heterogeneity for moderate to large-scale problems. Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally-efficient parallel Levenberg-Marquardt method for solving inverse modeling problems with a highly parameterized model space. Levenberg-Marquardt methods require the solution of a linear system of equations which can be prohibitively expensive to compute for moderate to large-scale problems. Our novel method projects the original linear problem down to a Krylov subspace, such that the dimensionality of the problem can be significantly reduced. Furthermore, we store the Krylov subspace computed when using the first damping parameter and recycle the subspace for the subsequent damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved using these computational techniques. We apply this new inverse modeling method to invert for random transmissivity fields in 2D and a random hydraulic conductivity field in 3D. Our algorithm is fast enough to solve for the distributed model parameters (transmissivity) in the model domain. The algorithm is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). By comparing with Levenberg-Marquardt methods using standard linear inversion techniques such as QR or SVD methods, our Levenberg-Marquardt method yields a speed-up ratio on the order of ~101 to ~102 in a multi-core computational environment. Furthermore, our new inverse modeling method is a powerful tool for characterizing subsurface heterogeneity for moderate- to large-scale problems. Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg-Marquardt method for solving inverse modeling problems with a highly parameterized model space. Levenberg-Marquardt methods require the solution of a linear system of equations which can be prohibitively expensive to compute for moderate to large-scale problems. Our novel method projects the original linear problem down to a Krylov subspace such that the dimensionality of the problem can be significantly reduced. Furthermore, we store the Krylov subspace computed when using the first damping parameter and recycle the subspace for the subsequent damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved using these computational techniques. We apply this new inverse modeling method to invert for random transmissivity fields in 2-D and a random hydraulic conductivity field in 3-D. Our algorithm is fast enough to solve for the distributed model parameters (transmissivity) in the model domain. The algorithm is coded in Julia and implemented in the MADS computational framework ( http://mads.lanl.gov ). By comparing with Levenberg-Marquardt methods using standard linear inversion techniques such as QR or SVD methods, our Levenberg-Marquardt method yields a speed-up ratio on the order. Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg‐Marquardt method for solving inverse modeling problems with a highly parameterized model space. Levenberg‐Marquardt methods require the solution of a linear system of equations which can be prohibitively expensive to compute for moderate to large‐scale problems. Our novel method projects the original linear problem down to a Krylov subspace such that the dimensionality of the problem can be significantly reduced. Furthermore, we store the Krylov subspace computed when using the first damping parameter and recycle the subspace for the subsequent damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved using these computational techniques. We apply this new inverse modeling method to invert for random transmissivity fields in 2‐D and a random hydraulic conductivity field in 3‐D. Our algorithm is fast enough to solve for the distributed model parameters (transmissivity) in the model domain. The algorithm is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). By comparing with Levenberg‐Marquardt methods using standard linear inversion techniques such as QR or SVD methods, our Levenberg‐Marquardt method yields a speed‐up ratio on the order of ∼101 to ∼102 in a multicore computational environment. Therefore, our new inverse modeling method is a powerful tool for characterizing subsurface heterogeneity for moderate to large‐scale problems. Key Points We generate a Krylov subspace and obtain a much smaller approximated problem by projecting the original problem down to the subspace We employ both coarse and fine‐grained parallelism to our LM method to parallelize the implementation of the LM algorithm in two levels We employ a subspace recycling technique to take the advantage of the solution space previously generated |
| Author | O'Malley, Daniel Vesselinov, Velimir V. Lin, Youzuo |
| Author_xml | – sequence: 1 givenname: Youzuo surname: Lin fullname: Lin, Youzuo email: ylin@lanl.gov organization: Los Alamos National Laboratory – sequence: 2 givenname: Daniel surname: O'Malley fullname: O'Malley, Daniel organization: Los Alamos National Laboratory – sequence: 3 givenname: Velimir V. surname: Vesselinov fullname: Vesselinov, Velimir V. organization: Los Alamos National Laboratory |
| BackLink | https://www.osti.gov/servlets/purl/1312574$$D View this record in Osti.gov |
| BookMark | eNp9kcFqHDEMhk1JoZu0tz6AaS-5TGPZM-PxMSxJW9gSWFpyNF6PZtdhxt7YnoTtqY-QZ8yT1GF7KD1UBwnE9_-S0Ck58cEjIe-BfQLG-AVn0N6uGSjGu1dkAaquK6mkOCELxmpRgVDyDTlN6Y4xqJtWLki-pDZM-zmb7II343igOAzOOvSZ7k0sHRzpCh_QbzBun389fTPxfjaxz9SM2xBd3k10CJHu3HZX1C-aCTNG9xN76vwDxoR0Cn2xMWXAIWF6S14PZkz47k89Iz-ur74vv1Srm89fl5erygjZtlUnoduojtuBKaUagL6srdrGAHIjpS25E8NgGzSNrVkre15vJPZGDqwcwcQZ-XD0DSk7nazLaHc2eI82axDAG1kX6PwI7WO4nzFlPblkcRyNxzAnDZ2QooZGyoJ-_Ae9C3MsRxVKMSVb4AL-S3UCOiVEwwsljtSjG_Gg99FNJh40MP3ySv33K_XternmJVrxGzy1li4 |
| ContentType | Journal Article |
| Copyright | Published 2016. This article is a U.S. Government work and is in the public domain in the USA. 2016. American Geophysical Union. All Rights Reserved. |
| Copyright_xml | – notice: Published 2016. This article is a U.S. Government work and is in the public domain in the USA. – notice: 2016. American Geophysical Union. All Rights Reserved. |
| CorporateAuthor | Los Alamos National Lab. (LANL), Los Alamos, NM (United States) |
| CorporateAuthor_xml | – name: Los Alamos National Lab. (LANL), Los Alamos, NM (United States) |
| DBID | 7QH 7QL 7T7 7TG 7U9 7UA 8FD C1K F1W FR3 H94 H96 KL. KR7 L.G M7N P64 OIOZB OTOTI |
| DOI | 10.1002/2016WR019028 |
| DatabaseName | Aqualine Bacteriology Abstracts (Microbiology B) Industrial and Applied Microbiology Abstracts (Microbiology A) Meteorological & Geoastrophysical Abstracts Virology and AIDS Abstracts Water Resources Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database AIDS and Cancer Research Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Meteorological & Geoastrophysical Abstracts - Academic Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts OSTI.GOV - Hybrid OSTI.GOV |
| DatabaseTitle | Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Virology and AIDS Abstracts Technology Research Database Aqualine Water Resources Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Meteorological & Geoastrophysical Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) ASFA: Aquatic Sciences and Fisheries Abstracts AIDS and Cancer Research Abstracts Engineering Research Database Industrial and Applied Microbiology Abstracts (Microbiology A) Meteorological & Geoastrophysical Abstracts - Academic |
| DatabaseTitleList | Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography Economics |
| EISSN | 1944-7973 |
| EndPage | 6977 |
| ExternalDocumentID | 1312574 4226768891 WRCR22226 |
| Genre | article |
| GrantInformation_xml | – fundername: Los Alamos National Laboratory (LANL) Director's Postdoctoral Fellowship – fundername: Los Alamos National Laboratory Environmental Programs Projects – fundername: the DiaMonD project (An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions, U.S. Department of Energy Office of Science funderid: 11145687 |
| GroupedDBID | -~X ..I .DC 05W 0R~ 123 1OB 1OC 24P 31~ 33P 50Y 5VS 6TJ 7WY 7XC 8-1 8CJ 8FE 8FG 8FH 8FL 8G5 8R4 8R5 8WZ A6W AAESR AAHBH AAIHA AAIKC AAMMB AAMNW AANHP AANLZ AASGY AAXRX AAYCA AAYJJ AAZKR ABCUV ABJCF ABJNI ABPPZ ABUWG ACAHQ ACBWZ ACCMX ACCZN ACGFO ACGFS ACIWK ACKIV ACNCT ACPOU ACPRK ACRPL ACXBN ACXQS ACYXJ ADBBV ADEOM ADKYN ADMGS ADNMO ADOZA ADXAS ADXHL ADZMN AEFGJ AEIGN AENEX AETEA AEUYN AEUYR AFBPY AFGKR AFKRA AFRAH AFWVQ AFZJQ AGQPQ AGXDD AIDBO AIDQK AIDYY AIQQE AIURR ALMA_UNASSIGNED_HOLDINGS ALUQN ALXUD AMYDB ASPBG ATCPS AVWKF AZFZN AZQEC AZVAB BDRZF BENPR BEZIV BFHJK BGLVJ BHPHI BKSAR BMXJE BPHCQ BRXPI CCPQU CS3 D0L D1J DCZOG DDYGU DPXWK DRFUL DRSTM DU5 DWQXO EBS EJD F5P FEDTE FRNLG G-S GNUQQ GODZA GROUPED_DOAJ GUQSH HCIFZ HVGLF HZ~ K60 K6~ L6V LATKE LEEKS LITHE LK5 LOXES LUTES LYRES M0C M2O M7R M7S MEWTI MSFUL MSSTM MVM MW2 MXFUL MXSTM MY~ O9- OHT OK1 P-X P2P P2W PALCI PATMY PCBAR PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PTHSS PUEGO PYCSY Q2X R.K RIWAO RJQFR ROL SAMSI SUPJJ TAE TN5 TWZ UQL VJK VOH WBKPD WIN WXSBR XOL XSW YHZ YV5 ZCG ZY4 ZZTAW ~02 ~KM ~OA ~~A 7QH 7QL 7T7 7TG 7U9 7UA 8FD C1K F1W FR3 H94 H96 KL. KR7 L.G M7N P64 A00 AAHHS ACCFJ AEEZP AEQDE AFPWT AIWBW AJBDE OIOZB OTOTI WYJ |
| ID | FETCH-LOGICAL-a3766-8718b982cf0999511d001965a1e2a77ce2a83ffc5ea5c4067d24b7eda7f0eff03 |
| IEDL.DBID | WIN |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000386977900014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0043-1397 |
| IngestDate | Tue Nov 05 04:33:40 EST 2024 Tue Oct 07 11:12:09 EDT 2025 Mon Sep 08 11:12:18 EDT 2025 Wed Aug 13 11:16:56 EDT 2025 Thu Sep 25 07:34:37 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a3766-8718b982cf0999511d001965a1e2a77ce2a83ffc5ea5c4067d24b7eda7f0eff03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE AC52-06NA25396 LANL EP Program LA-UR-16-22377 |
| OpenAccessLink | https://www.osti.gov/servlets/purl/1312574 |
| PQID | 1831893352 |
| PQPubID | 105507 |
| PageCount | 30 |
| ParticipantIDs | osti_scitechconnect_1312574 proquest_miscellaneous_1837341577 proquest_journals_1909761231 proquest_journals_1831893352 wiley_primary_10_1002_2016WR019028_WRCR22226 |
| PublicationCentury | 2000 |
| PublicationDate | September 2016 20160901 2016-09-01 |
| PublicationDateYYYYMMDD | 2016-09-01 |
| PublicationDate_xml | – month: 09 year: 2016 text: September 2016 |
| PublicationDecade | 2010 |
| PublicationPlace | Washington |
| PublicationPlace_xml | – name: Washington – name: United States |
| PublicationTitle | Water resources research |
| PublicationYear | 2016 |
| Publisher | John Wiley & Sons, Inc American Geophysical Union (AGU) |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: American Geophysical Union (AGU) |
| References | 2009; 45 2012 1982b; 8 2004; 27 2010 1998 1975; 12 2005; 41 1996 1992; 13 1994 2005 2004 2011; 37 2003 2002 2013; 8 1985; 21 1998; 86 2006; 216 1999 2010; 2010‐5169 1965; 2 2009; 11 2013; 59 1963; 11 2000 1986; 22 2015; 83 1982a; 8 1944; 2 1999; 35 2014; 36 2015 2014 2012; 48 2014; 51 2010; 6457 2007; 1 2014; 50 2007; 45 1988 |
| References_xml | – volume: 11 start-page: 110 year: 2009 end-page: 115 article-title: Damping‐undamping strategies for the Levenberg Marquardt nonlinear least squares method publication-title: Comput. Phys. – volume: 8 start-page: 195 year: 1982b end-page: 224 article-title: LSQR: Sparse linear equations and least squares problems publication-title: ACM Trans. Math. Software – volume: 37 start-page: 731 year: 2011 end-page: 738 article-title: A truncated Levenberg‐Marquardt algorithm for the calibration of highly parameterized nonlinear models publication-title: Comput. Geosci. – year: 2005 – volume: 45 start-page: W08405 year: 2009 article-title: Obtaining parsimonious hydraulic conductivity fields using head and transport observations: A Bayesian geostatistical parameter estimation approach publication-title: Water Resour. Res. – volume: 27 start-page: 737 year: 2004 end-page: 750 article-title: A modified Levenberg‐Marquardt algorithm for quasi‐linear geostatistical inversing publication-title: Adv. Water Resour. – volume: 35 start-page: 1975 year: 1999 end-page: 1985 article-title: Application of inverse methods to contaminant source identification from aquitard diffusion profiles at Dover AFB publication-title: Delaware, Water Resour. Res. – volume: 21 start-page: 1525 issue: 10 year: 1985 end-page: 1538 article-title: A comparison of several methods of solving nonlinear regression groundwater flow problems publication-title: Water Resour. Res. – year: 2003 – year: 1996 – year: 2000 – volume: 6457 start-page: 623 year: 2010 end-page: 632 article-title: Modified Levenberg Marquardt algorithm for inverse problems publication-title: Simulated Evol. Learning – volume: 41 start-page: W10412 year: 2005 article-title: A hybrid regularized inversion methodology for highly parameterized environmental models publication-title: Water Resour. Res. – volume: 50 start-page: 198 year: 2014 end-page: 207 article-title: Fast iterative implementation of large‐scale nonlinear geostatistical inverse modeling publication-title: Water Resour. Res. – year: 2014 – year: 1994 – year: 1998 – volume: 45 start-page: 254 year: 2007 end-page: 262 article-title: Are models too simple? Arguments for increased parameterization publication-title: Groundwater – year: 2010 – year: 2012 – volume: 8 start-page: 43 year: 1982a end-page: 71 article-title: LSQR: An algorithm for sparse linear equations and sparse least squares publication-title: ACM Trans. Math. Software – volume: 83 start-page: 127 year: 2015 end-page: 138 article-title: Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model publication-title: Comput. Geosci. – volume: 2010‐5169 year: 2010 article-title: Approaches to highly parameterized inversion: A guide to using PEST for groundwater‐model calibration publication-title: U.S. Geol. Surv. Sci. Invest. Rep. – volume: 48 start-page: W05522 year: 2012 article-title: Efficient methods for large‐scale linear inversion using a geostatistical approach publication-title: Water Resour. Res. – volume: 2 start-page: 164 year: 1944 end-page: 168 article-title: A method for the solution of certain non‐linear problems in least squares publication-title: Q. Appl. Math. – volume: 51 start-page: 4516 year: 2014 end-page: 4531 article-title: Linear functional minimization for inverse modeling publication-title: Water Resour. Res. – volume: 50 start-page: 5410 year: 2014 end-page: 5427 article-title: Large‐scale hydraulic tomography and joint inversion of head and tracer data using the principal component geostatistical approach (PCGA) publication-title: Water Resour. Res. – volume: 11 start-page: 431 year: 1963 end-page: 441 article-title: An algorithm for least‐squares estimation of nonlinear parameters, publication-title: J. Appl. Math. – volume: 1 year: 2007 – year: 2002 – year: 1988 – volume: 8 start-page: 0076665 year: 2013 article-title: Efficient parallel Levenberg‐Marquardt model fitting towards real‐time automated parametric imaging microscopy publication-title: PLOS ONE – year: 2004 – year: 2004 article-title: A variation of the Levenberg‐Marquardt method: An attempt to improve efficiency – volume: 22 start-page: 199 year: 1986 end-page: 210 article-title: Estimation of aquifer parameters under transient and steady state conditions: 1. Maximum likelihood method incorporating prior information publication-title: Water Resour. Res. – volume: 216 start-page: 707 issue: 2 year: 2006 end-page: 723 article-title: On level set regularization for highly ill‐posed distributed parameter estimation problems publication-title: J. Comput. Phys. – volume: 36 start-page: A1500 issue: 4 year: 2014 end-page: A1524 article-title: Active subspace methods in theory and practice: Applications to kriging surfaces publication-title: SIAM J. Sci. Comput. – volume: 2 start-page: 205 issue: 2 year: 1965 end-page: 224 article-title: Calculating the singular values and pseudo‐inverse of a matrix publication-title: J. Soc. Ind. Appl. Math. – start-page: 54 year: 2015 – volume: 13 start-page: 771 issue: 3 year: 1992 end-page: 793 article-title: A parallel nonlinear least‐squares solver: Theoretical analysis and numerical results publication-title: SIAM J. Sci. Stat. Comput. – year: 2015 – volume: 12 start-page: 617 year: 1975 end-page: 629 article-title: Solution of sparse indefinite systems of linear equations publication-title: SIAM J. Numer. Anal. – volume: 86 start-page: 61 year: 1998 end-page: 81 article-title: Using an inverse method to estimate the hydraulic properties of crusted soils from tension‐disc infiltrometer data publication-title: Geoderma – year: 1999 – volume: 50 start-page: 5428 year: 2014 end-page: 5443 article-title: Principal component geostatistical approach for large‐dimensional inverse problem publication-title: Water Resour. Res. – volume: 59 start-page: 6587 year: 2013 end-page: 6600 article-title: Geostatistical reduced‐order models in underdetermined inverse problems publication-title: Water Resour. Res. |
| SSID | ssj0014567 |
| Score | 2.3066766 |
| Snippet | Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the... |
| SourceID | osti proquest wiley |
| SourceType | Open Access Repository Aggregation Database Publisher |
| StartPage | 6948 |
| SubjectTerms | Algorithms Computational efficiency Computer applications Damping dimensionality reduction Earth Sciences Efficiency Environment models Fields Frameworks Heterogeneity Hydraulic conductivity hydraulic inverse modeling Krylov subspace approximation Levenberg‐Marquardt method LSQR iterative method Mathematical models MATHEMATICS AND COMPUTING Methods Modelling Parameterization Parameters Problems Software subspace recycling Three dimensional models Transmissivity |
| Title | A computationally efficient parallel Levenberg‐Marquardt algorithm for highly parameterized inverse model analyses |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2F2016WR019028 https://www.proquest.com/docview/1831893352 https://www.proquest.com/docview/1909761231 https://www.proquest.com/docview/1837341577 https://www.osti.gov/servlets/purl/1312574 |
| Volume | 52 |
| WOSCitedRecordID | wos000386977900014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library - Journals customDbUrl: eissn: 1944-7973 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014567 issn: 0043-1397 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1944-7973 dateEnd: 20231213 omitProxy: false ssIdentifier: ssj0014567 issn: 0043-1397 databaseCode: WIN dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS-RAEC5EBffiY3Vx1gct7NFguvPozFFcBwUdZFgZb6G709GBmNFJXHBP-xP8jf6SrepkZkfwIl5CoFOhobqqvu6u-grgB_eNrxVP0PtFoUf85Z5SUntBLkzEleBZpl2zCdnvJzc33av2wI1qYRp-iNmBG1mG89dk4EpXR_9JQzFyxcMBlUILqvXlobPL4Xl_domA2EBOL5gJ6LR57yh-NC-MzniM5vQGYs4DVRdpemufneM6rLYYkx03i2IDFmz5FVamJcgVvretz--eN6E-ZsZ1dmhPBYtnZh2tBEYjRsTgRWELdkFET5QK9vr35VJNHmlh1UwVt-PJqL67Zwh9GTEfozTJ3FOSzeiPzdiopLwPy1zHHaYcBYqttuC6d_rr5MxrWzF4Cj1QjD6TJ7qbCJMTokSQljVchIpboaSkrmJJkOcmsioyiBFkJkItbaZk7uOk_eAbLJbj0m4DMyq0Ah--r6NQRlrjhoV2NYb7Kg5M3IEdUkeKCIBobA3l-5g65QFCMRl2YHeqpbS1tipFt8QRdyGWfH-46yPowhDNO3AwG0YzorsRVdrxk_uFxIAeSdmBQ6fS9KGh-0gbYmeRziszHQ5OBgitRPz9Y5_vwBcaaLLUdmGxnjzZPVg2v-tRNdmHpZ-D3vXFvlvH_wA-ufOH |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1fT9swED-xDgleYLBNlDJmpD0uInb-OH1ECASiVFPFVN4sx3GgUkhHG5DgiY_AZ-STcOekpZP2MvESWbIvsnS-u5_t8-8AfnDf-KnmCXq_KPSIv9zTWqZekAsTcS14lqWu2ITs95PLy-6vps4pvYWp-SHmB25kGc5fk4HTgfT-G2sohq54OKC30CL5AB_DOOwSef7wtD-_RkB0IGdXzAR1msx3lN9flEZ3PEaD-gtkLkJVF2uO1989y0-w1sBMdlCviw1YsuUmrMxeIU-x3VQ_v374DNUBM664Q3MwWDww65glMCAx4gYvCluwHnE9UTbYy9PzuZ7c0tqqmC6uxpNRdX3DEP0yIj9GaZK5oTyb0aPN2Kik1A_LXNEdph0Lip1-gd_HRxeHJ15TjcHT6IRidJs8SbuJMDmBSsRpWU1HqLkVWkoqLJYEeW4iqyODMEFmIkylzbTMfZy0H3yFVjku7RYwo0Mr8OP7aRTKKE1xz0IbG8N9HQcmbkOH9KEQBBCTraGUH1MpHiAak2EbdmZqUo3BTRV6Jo7QC-Hkv7u7PuIujNK8DXvzbrQkuh7RpR3fuV9IjOmRlG346XSq_tSMH6rmdhZqUZlqODgcILoS8fb_Df8OKycX5z3VO-2fdWCVBtVJazvQqiZ39hssm_tqNJ3susX8CvFN9hI |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB4ViigXKC-x5VFX4khE7DycPSJgVVRYoVUR3CzHcdiVQpbuBiQ49Sf0N_aXMONkt4vEpeISRbInijSvz_b4G4B97hs_1TzB6BeFHvGXe1rL1AtyYSKuBc-y1DWbkN1ucnPTvmz6nNJdmJofYrrhRp7h4jU5uL3P8sN_rKGYuuLrHt2FFskcfAxjtF66UnLWnR4jIDqQkyNmgjpN5TvKH85KYzgeokO9ApmzUNXlms7Ku__yMyw3MJMd1XaxCh9suQafJreQx_jedD_vP61DdcSMa-7QbAwWT8w6ZglMSIy4wYvCFuycuJ6oGuzv7z8XevSLbKtiurgdjgZV_44h-mVEfozSJHNHdTaDZ5uxQUmlH5a5pjtMOxYUO96Aq87pz-PvXtONwdMYhGIMmzxJ24kwOYFKxGlZTUeouRVaSmoslgR5biKrI4MwQWYiTKXNtMx9_Gk_2IT5cljaLWBGh1bgw_fTKJRRmuKahRY2hvs6Dkzcgm3Sh0IQQEy2hkp-TKV4gGhMhi3YmahJNQ43VhiZOEIvhJNvD7d9xF2YpXkLvk2H0ZPoeESXdvjgPiExp0dStuDA6VTd14wfquZ2FmpWmeq6d9xDdCXiL_83_SssXp501PlZ98c2LNGcumZtB-ar0YPdhQXzWA3Goz1nyy9wu_WE |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+computationally+efficient+parallel+Levenberg-Marquardt+algorithm+for+highly+parameterized+inverse+model+analyses&rft.jtitle=Water+resources+research&rft.au=Lin%2C+Youzuo&rft.au=O%27Malley%2C+Daniel&rft.au=Vesselinov%2C+Velimir+V.&rft.date=2016-09-01&rft.pub=American+Geophysical+Union+%28AGU%29&rft.issn=0043-1397&rft_id=info:doi/10.1002%2F2016WR019028&rft.externalDocID=1312574 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0043-1397&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0043-1397&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0043-1397&client=summon |