[Ahopelto J. and Haatainen T. (2002). Step and flash imprint lithography, in C.M. Sotomayor Torres (Ed.), Alternative Lithography. Unleashing the Potentials of Nanotechnology, Kluwer Academic Publisher, Boston, MA/Dortrecht/London, Chapter 6, pp. 127-142.]Search in Google Scholar
[Alvarez-Aramberri, J., Pardo, D. and Barucq, H. (2013). Inversion of magnetotelluric measurements using multigoal oriented hp-adaptivity, Procedia Computer Science 18(8): 1564-1573.10.1016/j.procs.2013.05.324]Search in Google Scholar
[Babuška, I. and Guo, B. (1986a). The hp-version of the finite element method, Part I: The basic approximation results, Computational Mechanics 1(1): 21-41.10.1007/BF00298636]Search in Google Scholar
[Babuška, I. and Guo, B. (1986b). The hp-version of the finite element method, Part II: General results and applications, Computational Mechanics 1(3): 203-220.10.1007/BF00272624]Search in Google Scholar
[Banks, H.T. and Kunisch, K. (1989). Estimation Techniques for Distributed Parameter Systems, Birkhäuser, Boston, MA.10.1007/978-1-4612-3700-6]Search in Google Scholar
[Barabasz, B., Gajda, E., Mig´orski, S., Paszy´nski, M. and Schaefer, R. (2011a). Studying inverse problems in elasticity by hierarchic genetic search, IPM’2011 Conference Proceedings, Sieniawa, Poland, pp. 9-10.]Search in Google Scholar
[Barabasz, B., Migórski, S., Schaefer, R. and Paszy ski, M. (2011b). Multi-deme, twin adaptive strategy hp-HGS, Inverse Problems in Science and Engineering 19(1): 3-16.10.1080/17415977.2010.531477]Search in Google Scholar
[Barabasz, B., Schaefer, R. and Paszyński, M. (2009). Handling ambiguous inverse problems by the adaptive genetic strategy hp-HGS, in G. Allen, J. Nabrzyski, E. Seidel, G.D. van Albada, J. Dongarra and P.M.A. Sloot (Eds.) Computational Science ICCS 2009, 9th International Conference, Baton Rouge, LA, USA, May 25-27, 2009, Proceedings, Part II, Lecture Notes in Computer Sience, Vol. 5545, Springer-Verlag, Berlin/Heidelberg, pp. 904-913.10.1007/978-3-642-01973-9_100]Search in Google Scholar
[Burczyński, T. and Beluch, W. (2001). The identification of cracks using boundary elements and evolutionary algorithms, Engineering Analysis with Boundary Elements 25(4-5): 313-322.10.1016/S0955-7997(01)00027-3]Search in Google Scholar
[Burczyński, T., Kuś, W., Długosz, A. and Orantek, P. (2004). Optimization and defect identification using distributed evolutionary algorithms, Engineering Applications of Artificial Intelligence 17(4): 337-344.10.1016/j.engappai.2004.04.007]Search in Google Scholar
[Burns, R., Johnson, S., Schmid, G., Kim, E., Dickey, D., Meiring, J., Burns, S., Stacey, N., Willson, C., Convey, D., Wei, Y., Fejes, P., Gehoski, K., Mancini, D. Nordquist, K., Dauksher, W.J and Resnick, D.J. (2004). Mesoscale modeling for SFIL simulating polymerization kinetics and densification, Proceedings of SPIE 2004, Santa Clara, CA, USA, Vol. 5374, pp. 348-360.]Search in Google Scholar
[Cabib, E., Davini, C. and Chong-Quing, R. (1990). A problem in the optimal design of networks under transverse loading, Quarterly of Applied Mathematics 48(2): 251-263.10.1090/qam/1052135]Search in Google Scholar
[Cabib, E., Schaefer, R. and Telega, H. (1998). A parallel genetic clustering for inverse problems, in B. Kagstr¨om, J. Dongarra, E. Elmroth and J. Wasniewski (Eds.), Applied Parallel Computing. Large Scale Scientific and Industrial Problems. 4th International Workshop, PARA’98, Umea, Sweden, June 14-17, Proceedings, Lecture Notes in Computer Science, Vol. 1541, Springer, Berlin/Heidelberg, pp. 551-556.]Search in Google Scholar
[Caicedo, J.M. and Yun, G. (2011). A novel evolutionary algorithm for identifying multiple alternative solutions in model updating, Structural Health Monitoring 10(5): 491-501.10.1177/1475921710381775]Search in Google Scholar
[Cantú-Paz, E. (2000). Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, Norwell, MA. 10.1007/978-1-4615-4369-5]Search in Google Scholar
[Chase Geoffrey, J., Barroso, Luciana, R. and Hwank, K.-S. (2004). LMS-based structural health monitoring methods for the ASCE benchmark problem, Proceedings of the 2004 American Control Conference, Boston, MA, USA, Vol. 5, pp. 4201-4206.]Search in Google Scholar
[Ciarlet, G. (1978). The Finite Element Method for Elliptic Problems, North Holland, Amsterdam.10.1115/1.3424474]Search in Google Scholar
[Colburn, M. (1978). Step and Flash Imprint Lithography: A Low Pressure, Room Temperature Nonoimprint Lithography, Ph.D. thesis, University of Texas, Austin, TX.]Search in Google Scholar
[Colburn, M., Suez, I., Choi, B., Meissi, M., Bailey, T., Sreeni-vasan, S., Ekerdt, J. and Willson, C. (2001). Characterization and modeling of volumetric and mechanical properties for SFIL photopoly-mers, Journal of Vacuum Science and Technology 19(6): 2685-2689.10.1116/1.1420199]Search in Google Scholar
[Demkowicz, L. (2006). Computing with hp-Adaptive Finite Elements, Vol. I: One and Two Dimensional Elliptic and Maxwell Problems, Chapman and Hall/CRC Applied Mathematics and Nonlinear Science, London.10.1201/9781420011685]Search in Google Scholar
[Demkowicz, L., Kurtz, J., Pardo, P., Paszyński, M., Rachowicz, W. and Zdunek, A. (2007). Computing with hp- Adaptive Finite Elements, Vol. II: Frontiers: Three- Dimensional Elliptic and Maxwell Problems with Applications, Chapman and Hall/CRC Applied Mathematics and Nonlinear Science, London.10.1201/9781420011692]Search in Google Scholar
[Denkowski, Z., Migórski, S. and Papageorgiou, N. (2003a). An Introduction to Nonlinear Analysis: Applications, Kluwer Academic/Plenum, New York, NY.10.1007/978-1-4419-9156-0]Search in Google Scholar
[Denkowski, Z., Migórski, S. and Papageorgiou, N. (2003b). An Introduction to Nonlinear Analysis: Theory, Kluwer Academic/Plenum, New York, NY.10.1007/978-1-4419-9158-4]Search in Google Scholar
[Descloux, J. (1973). Méthode Des Élèments Finis, Ecole Polytechnique Fédérale de Lausanne, Lausanne.]Search in Google Scholar
[Engl, H.W., Hanke, M. and Neubauer, A. (2000). Regularization of Inverse Problems, Kluwer, Dordrecht.10.1007/978-94-009-1740-8_3]Search in Google Scholar
[Figueiredo, E., Park, G., Farrar, C.R.,Worden, K. and Figueiras, J. (2011). Machine learning algorithms for damage detection under operational and environmental variability, Structural Health Monitoring 10(6): 559-572.10.1177/1475921710388971]Search in Google Scholar
[Friswell, M.I. and Mottershead, J.E. (2001). Inverse methods in structural health monitoring, Key Engineering Materials 204-205: 201-210.10.4028/www.scientific.net/KEM.204-205.201]Search in Google Scholar
[Garibaldi, L., Surace, C., Holford, K. and Ostachowicz, W.M. (1999). Damage Assessment of Structures, Trans Tech Publications, Zürich.]Search in Google Scholar
[Glover, F. and Kochenberger, G. (2002). Handbook of Metaheuristics, Kluwer Academic Publishers, Dordrecht.10.1007/b101874]Search in Google Scholar
[Horst, R. and Pardalos, P. (1995). Handbook of Global Optimization, Kluwer, Dordrecht.10.1007/978-1-4615-2025-2]Search in Google Scholar
[Hughes, T. (2000). The Finite Element Method. Linear Statics and Dynamic Finite Element Analysis, Dover Publications, Mineola, NY.]Search in Google Scholar
[Huhtala, A. and Sven, B. (2011). A Bayesian approach to vibration based structural health monitoring with experimental verification, Rakenteiden Mekaniikka (Journal of Structural Mechanics) 44(4): 330-344.]Search in Google Scholar
[Isakov, V. (2006). Inverse Problems for Partial Differential Equations, Springer, New York, NY.]Search in Google Scholar
[Kirikera, Goutham, R., Shinde, V., Schulz, Mark, J., Ghoshal, A., Sundaresan, Mannur, J., Allemang, Randall, J. and Won Lee, J. (2008). A structural neural system for real-time health monitoring of composite materials, Structural Health Monitoring 7(1): 65-83.10.1177/1475921707081971]Search in Google Scholar
[Koper, K., Wysession, M. and Wiens, D. (1999). Multimodal function optimization with a niching genetic algorithm: A seismological example, Bulletin of the Seismological Society of America 89(4): 978-988.]Search in Google Scholar
[Lashin, S. and Likoshvai, V. (2004). Evolutionary algorithms for mathematical models of gene regulatory networks, Proceedings of the 4th International Conference on Bioinformatics of Genome Regulation and Structure, BGRS 2004, Novosibirsk, Russia, Vol. 2, pp. 81-84.]Search in Google Scholar
[Mahfoud, S. (1997). Niching methods, in T. Back, D.B. Fogel and Z. Michalewicz (Eds.), Handbook of Evolutionary Computations, Oxford University Press, Oxford, Chapter C.6.1, pp. C6.1:1-C6.1:4.]Search in Google Scholar
[Meruane, V. and Heylen, W. (2009). Damage detection with parallel genetic algorithms and operational modes, Structural Health Monitoring 9(6): 481-496.10.1177/1475921710365400]Search in Google Scholar
[Oden, J.T. and Prudhomme, S. (2001). Goal-oriented error estimation and adaptivity for the finite element method, Computers and Mathematics with Applications 41(5): 735-756.10.1016/S0898-1221(00)00317-5]Search in Google Scholar
[Osman, I. and Kelly, J. (1996). Meta-Heuristics: Theory and Applications, Kluwer Academic Publishers, Norwell, MA. Osman, I.H. and Laporte, G. (1996). Metaheuristics: A bibliography, Annals of Operations Research 63(5): 511-623.10.1007/978-1-4613-1361-8]Search in Google Scholar
[Paszyńska, A., Grabska, E. and Paszyński, M. (2012a). A graph grammar model of the hp adaptive three dimensional finite element method, Part I, Fundamenta Informaticae 114(2): 149-182.10.3233/FI-2012-622]Search in Google Scholar
[Paszyńska, A., Grabska, E. and Paszyński, M. (2012b). A graph grammar model of the hp adaptive three dimensional finite element method, Part II, Fundamenta Informaticae 114(2): 183-201.10.3233/FI-2012-623]Search in Google Scholar
[Paszyńska, A., Paszyński, M. and Grabska, E. (2008). Graph transformations for modeling hp-adaptive finite element method with triangular elements, in M. Bubak, G.D. van Albada, J. Dongarra and P.M.A. Sloot (Eds.) Computational Science-ICCS 2008, 8th International Conference, Krak´ow, Poland, June 23-25, Proceedings, Part III, Lecture Notes in Computer Science, Vol. 5103, Springer, Berlin, pp. 604-613.10.1007/978-3-540-69389-5_68]Search in Google Scholar
[Paszyńska, A., Paszyński, M. and Grabska, E. (2009). Graph transformations for modeling hp-adaptive finite element method with mixed triangular and rectangular elements, in G. Allen, J. Nabrzyski, E. Seidel, G.D. van Albada, J. Dongarra and P.M.A. Sloot (Eds.), Computational Science-ICCS 2009, 9th International Conference, Baton Rouge, LA, USA, Proceedings, Part II, Lecture Notes in Computer Science, Vol. 5545, Springer, Berlin, pp. 875-884.10.1007/978-3-642-01973-9_97]Search in Google Scholar
[Paszyński, M. (2009a). On the parallelization of self-adaptive hp-finite element methods, Part I: Composite programmable graph grammar model, Fundamenta Informaticae 93(4): 411-434.10.3233/FI-2009-111]Search in Google Scholar
[Paszyński, M. (2009b). On the parallelization of self-adaptive hp-finite element methods, Part II: Partitioning communication agglomeration mapping (PCAM) analysis, Fundamenta Informaticae 93(4): 435-457.10.3233/FI-2009-112]Search in Google Scholar
[Paszyński, M., Barabasz, B. and Schaefer, R. (2007). Efficient adaptive strategy for solving inverse problems, in Y. Shi, G.D. van Albada, J. Dongarra and P.M.A. Sloot (Eds.), Computational Science-ICCS 2007. 7th International Conference, Beijing China, May 27-30, 2007, Proceedings, Part I, Lecture Notes in Computer Science, Vol. 4487, Springer, Berlin, pp. 342-349.10.1007/978-3-540-72584-8_44]Search in Google Scholar
[Paszyński, M. and Demkowicz, L. (2006). Parallel fully automatic hp-adaptive 3D finite element package, Engineering with Computers 22(3-4): 255-276.10.1007/s00366-006-0036-8]Search in Google Scholar
[Paszyński, M., Kurtz, J. and Demkowicz, L. (2006). Parallel fully automatic hp-adaptive 2D finite element package, Computer Methods in Applied Mechanics and Engineering 195(7-8): 711-741.10.1016/j.cma.2005.02.019]Search in Google Scholar
[Paszyński, M., Gurgul, P., Sieniek, M. and Pardo, D. (2010a). Unified modeling language description of the object-oriented multi-scale adaptive finite element method for step-and-flash imprint lithography, IOP Conference Series: Materials Science and Engineering 10(1): 012247.10.1088/1757-899X/10/1/012247]Search in Google Scholar
[Paszyński, M., Pardo, D. and Paszyńska, A. (2010b). Parallel multi-frontal solver for p adaptive finite element modeling of multi-physics computational problems, Journal of Computational Science 1(1): 48-54.10.1016/j.jocs.2010.03.002]Search in Google Scholar
[Paszyński, M., Romkes, A., Collister, E., Meiring, J., Demkowicz, L. and Willson, C. (2005). On the modeling of step-and-flash imprint lithography using molecular statics models, Technical Report 05-38, ICES, Austin, TX.]Search in Google Scholar
[Paszyński, M. and Schaefer, R. (2010). Graph grammar-driven parallel partial differential equation solver, Concurrency and Computation: Practice and Experience 22(9): 1063-1097.10.1002/cpe.1533]Search in Google Scholar
[Rachowicz, W., Pardo, D. and Demkowicz, L. (2006). Fully automatic hp-adaptivity in three dimensions, Computer Methods in Applied Mechanics and Engineering 195(37-40): 4816-4842.10.1016/j.cma.2005.08.022]Search in Google Scholar
[Rocca, P., Benedetti, M., Donelli, M., Franceschini, D. and Massa, A. (2009). Evolutionary optimization as applied to inverse scattering problems, Inverse Problems 25(12): 123003.10.1088/0266-5611/25/12/123003]Search in Google Scholar
[Ryszka, I., Paszyńska, A., Grabska, E. and Paszyński, M. (2013). Graph grammar systems for modeling three dimensional finite element method, Fundamenta Informaticae, (submitted).10.3233/FI-2012-622]Search in Google Scholar
[Samarski, A.A. and Vabishchevich, P.N. (2007). Numerical Methods for Solving Inverse Problems of Mathematical Physics, Walter de Gruyter, Berlin.10.1515/9783110205794]Search in Google Scholar
[Schaefer, R. and Barabasz, B. (2008). Asymptotic behavior of hp-HGS (hp-adaptive finite element method coupled with the hierarchic genetic strategy) by solving inverse problems, in M. Bubak, G.D. van Albada, J. Dongarra, and P.M.A. Sloot (Eds.), Computational Science-ICCS10.1007/978-3-540-69389-5_76]Search in Google Scholar
[2008. 8th International Conference, Kraków, Poland, June 23-25, 2008, Proceedings, Part III, Lecture Notes in Computer Science, Vol. 5103, Springer, Berlin/Heidelberg, pp. 682-692.]Search in Google Scholar
[Schaefer, R. and Kołodziej, J. (2003). Genetic search reinforced by the population hierarchy, in K. DeJong, R. Poli and J. Rowe (Eds.), Foundations of Genetic Algorithms 7, Morgan Kaufman, Burlington, MA, pp. 383-399.]Search in Google Scholar
[Schwab, C. (1998). p and hp Finite Element Methods, Oxford University Press, Oxford.]Search in Google Scholar
[Singh, A.,Minsker, B. and Takagi, H. (2006). Interactive genetic algorithms for inverse groundwater modeling: Issues with human fatigue and prediction models, in R. Walton (Ed.), Proceedings of the 2005 World Water and Environmental Resources Congress: Impacts of Global Climate Change, May 15-19, 2005, Anchorage, AK, Vol. 5, American Society of Civil Engineers, Reston, VA, pp. 3081-3092.]Search in Google Scholar
[Strug, B., Paszyńska, A., Paszyński, M. and Grabska, E. (2013). Using a graph grammar system in the finite element method, International Journal of Applied Mathematics and Computer Science 23(4): 839-853, DOI:10.2478/amcs-2013-0063.10.2478/amcs-2013-0063]Search in Google Scholar
[Tanaka, M. (Ed.) (2003). Inverse Problems in Engineering Mechanics IV. Proceedings of the International Symposium on Inverse Problems in Engineering Mechanics 2003 (ISP 2003), Nagano, Japan, Elsevier, Amsterdam.]Search in Google Scholar
[Tarantola, A. (2005). Inverse Problem Theory and Methods for Model Parameter Estimation, SIAM, Philadelphia, PA.10.1137/1.9780898717921]Search in Google Scholar
[Tikhonov, A.N., Goncharskii, A., Stepanov, V.V. and Yagola, A.G. (1995). Numerical Methods for the Solution of Ill- Posed Problems, Mathematics and Its Applications, Vol. 328, Springer-Verlag, Berlin/Heidelberg.]Search in Google Scholar
[Vose, M. (1999). The Simple Genetic Algorithm, MIT Press, Boston, MA.]Search in Google Scholar
[Wierzba, B., Semczuk, A., Kołodziej, J. and Schaefer, R. (2003). Hierarchical genetic strategy with real number encoding, Proceedings of the 6th Conference on Evolutionary Algorithms and Global Optimization, Łag´ow Lubuski, Poland, pp. 231-237.]Search in Google Scholar
[Xavier, C., Vieira, V., Martins, D. and Dos Santos, R. (2006). Comparing two parallel genetic algorithms for the inverse problem associated to the cardiac bidomain equations, Workshop on High Performance Computing in the Life Sciences, HPC LIFE, Ouru Preto, Brazil.]Search in Google Scholar
[Zhu, C., Byrd, R. H., Lu, P. and Nocedal, J. (1997). Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM Transactions on Mathematical Software 23(4): 550-560. 10.1145/279232.279236]Search in Google Scholar