Towards Bifurcation Detection in Kinetic Monte Carlo Simulations: Robust Identification with Artificial Neural Networks and Nonlinear Kalman Filters
The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detectio...
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
The Berkeley Electronic Press ; 2006 |
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| Reproduktion: |
Berkeley Electronic Press Academic Journals |
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| Übergeordnetes Werk: |
In: International journal of chemical reactor engineering - Berkeley, Calif. : Bepress, 2003, 3.2006, 1, A63 |
| Übergeordnetes Werk: |
volume:3 ; year:2006 ; number:1 ; pages:63 |
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| 520 | |a The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. | ||
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(DE-627)NLEJ219557187 DE-627 ger DE-627 rakwb eng XD-US Towards Bifurcation Detection in Kinetic Monte Carlo Simulations: Robust Identification with Artificial Neural Networks and Nonlinear Kalman Filters The Berkeley Electronic Press 2006 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. Berkeley Electronic Press Academic Journals bifurcation Monte Carlo identification González-Figueredo, Carlos oth Rico-Martínez, Ramiro oth In International journal of chemical reactor engineering Berkeley, Calif. : Bepress, 2003 3.2006, 1, A63 Online-Ressource (DE-627)NLEJ219537194 (DE-600)2112754-2 1542-6580 nnns volume:3 year:2006 number:1 pages:63 http://www.bepress.com/ijcre/vol3/A63 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2006 1 63 3.2006, 1, A63 |
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(DE-627)NLEJ219557187 DE-627 ger DE-627 rakwb eng XD-US Towards Bifurcation Detection in Kinetic Monte Carlo Simulations: Robust Identification with Artificial Neural Networks and Nonlinear Kalman Filters The Berkeley Electronic Press 2006 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. Berkeley Electronic Press Academic Journals bifurcation Monte Carlo identification González-Figueredo, Carlos oth Rico-Martínez, Ramiro oth In International journal of chemical reactor engineering Berkeley, Calif. : Bepress, 2003 3.2006, 1, A63 Online-Ressource (DE-627)NLEJ219537194 (DE-600)2112754-2 1542-6580 nnns volume:3 year:2006 number:1 pages:63 http://www.bepress.com/ijcre/vol3/A63 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2006 1 63 3.2006, 1, A63 |
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(DE-627)NLEJ219557187 DE-627 ger DE-627 rakwb eng XD-US Towards Bifurcation Detection in Kinetic Monte Carlo Simulations: Robust Identification with Artificial Neural Networks and Nonlinear Kalman Filters The Berkeley Electronic Press 2006 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. Berkeley Electronic Press Academic Journals bifurcation Monte Carlo identification González-Figueredo, Carlos oth Rico-Martínez, Ramiro oth In International journal of chemical reactor engineering Berkeley, Calif. : Bepress, 2003 3.2006, 1, A63 Online-Ressource (DE-627)NLEJ219537194 (DE-600)2112754-2 1542-6580 nnns volume:3 year:2006 number:1 pages:63 http://www.bepress.com/ijcre/vol3/A63 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2006 1 63 3.2006, 1, A63 |
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(DE-627)NLEJ219557187 DE-627 ger DE-627 rakwb eng XD-US Towards Bifurcation Detection in Kinetic Monte Carlo Simulations: Robust Identification with Artificial Neural Networks and Nonlinear Kalman Filters The Berkeley Electronic Press 2006 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. Berkeley Electronic Press Academic Journals bifurcation Monte Carlo identification González-Figueredo, Carlos oth Rico-Martínez, Ramiro oth In International journal of chemical reactor engineering Berkeley, Calif. : Bepress, 2003 3.2006, 1, A63 Online-Ressource (DE-627)NLEJ219537194 (DE-600)2112754-2 1542-6580 nnns volume:3 year:2006 number:1 pages:63 http://www.bepress.com/ijcre/vol3/A63 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2006 1 63 3.2006, 1, A63 |
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(DE-627)NLEJ219557187 DE-627 ger DE-627 rakwb eng XD-US Towards Bifurcation Detection in Kinetic Monte Carlo Simulations: Robust Identification with Artificial Neural Networks and Nonlinear Kalman Filters The Berkeley Electronic Press 2006 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. Berkeley Electronic Press Academic Journals bifurcation Monte Carlo identification González-Figueredo, Carlos oth Rico-Martínez, Ramiro oth In International journal of chemical reactor engineering Berkeley, Calif. : Bepress, 2003 3.2006, 1, A63 Online-Ressource (DE-627)NLEJ219537194 (DE-600)2112754-2 1542-6580 nnns volume:3 year:2006 number:1 pages:63 http://www.bepress.com/ijcre/vol3/A63 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2006 1 63 3.2006, 1, A63 |
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The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. |
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The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. |
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The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ219557187</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506181739.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">090716s2006 xxu|||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ219557187</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XD-US</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Towards Bifurcation Detection in Kinetic Monte Carlo Simulations: Robust Identification with Artificial Neural Networks and Nonlinear Kalman Filters</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="b">The Berkeley Electronic Press</subfield><subfield code="c">2006</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Berkeley Electronic Press Academic Journals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bifurcation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Monte Carlo</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">identification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">González-Figueredo, Carlos</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rico-Martínez, Ramiro</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International journal of chemical reactor engineering</subfield><subfield code="d">Berkeley, Calif. : Bepress, 2003</subfield><subfield code="g">3.2006, 1, A63</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ219537194</subfield><subfield code="w">(DE-600)2112754-2</subfield><subfield code="x">1542-6580</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2006</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:63</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.bepress.com/ijcre/vol3/A63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-BEP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2006</subfield><subfield code="e">1</subfield><subfield code="h">63</subfield><subfield code="y">3.2006, 1, A63</subfield></datafield></record></collection>
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