Artificial Neural Networks for dynamic optimization of stochastic multiscale systems subject to uncertainty
暂无分享,去创建一个
[1] Shabnam Rasoulian,et al. A robust nonlinear model predictive controller for a multiscale thin film deposition process , 2015 .
[2] M. Stamatakis,et al. A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior , 2011 .
[3] Eric Croiset,et al. Carbon nanotube growth: First-principles-based kinetic Monte Carlo model , 2015 .
[4] P. R. Westmoreland,et al. Hierarchical Reduced Models for Catalytic Combustion: H2/Air Mixtures Near Platinum Surfaces , 1997 .
[5] Huang Kai,et al. Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm , 2003 .
[6] P. Christofides,et al. Multiscale modeling and operation of PECVD of thin film solar cells , 2015 .
[7] J. Kwon,et al. Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems , 2019, AIChE Journal.
[8] Prashant Mhaskar,et al. Subspace Identification Based Modeling and Control of Batch Particulate Processes , 2017, Modeling and Control of Batch Processes.
[9] Berend Smit,et al. Understanding molecular simulation: from algorithms to applications , 1996 .
[10] Panagiotis D. Christofides,et al. Multiscale computational fluid dynamics modeling of thermal atomic layer deposition with application to chamber design , 2019, Chemical Engineering Research and Design.
[11] J. Kwon,et al. Multiscale modeling and control of Kappa number and porosity in a batch‐type pulp digester , 2019, AIChE Journal.
[12] Dionisios G. Vlachos,et al. Low-Dimensional Approximations of Multiscale Epitaxial Growth Models for Microstructure Control of Materials , 2000 .
[13] Kan Wu,et al. Optimization of simultaneously propagating multiple fractures in hydraulic fracturing to achieve uniform growth using data-based model reduction , 2018, Chemical Engineering Research and Design.
[14] Curtis B. Storlie,et al. Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System , 2014 .
[15] P. Christofides,et al. Multiscale modeling and run-to-run control of PECVD of thin film solar cells , 2017 .
[16] Geoffrey E. Hinton,et al. A time-delay neural network architecture for isolated word recognition , 1990, Neural Networks.
[17] Geoffrey E. Hinton,et al. Temporal-Kernel Recurrent Neural Networks , 2010, Neural Networks.
[18] Shabnam Rasoulian,et al. Robust multivariable estimation and control in an epitaxial thin film growth process under uncertainty , 2015 .
[19] A. Boudouvis,et al. Multiscale modeling in chemical vapor deposition processes: Coupling reactor scale with feature scale computations , 2010 .
[20] Venkat Venkatasubramanian,et al. Design of Fuel Additives Using Neural Networks and Evolutionary Algorithms , 2001 .
[21] T. Trucano,et al. Verification, Validation, and Predictive Capability in Computational Engineering and Physics , 2004 .
[22] Joseph Sang-Il Kwon,et al. Data-driven identification of interpretable reduced-order models using sparse regression , 2018, Comput. Chem. Eng..
[23] Panagiotis D. Christofides,et al. Multiscale, Multidomain Modeling and Parallel Computation: Application to Crystal Shape Evolution in Crystallization , 2015 .
[24] Panagiotis D. Christofides,et al. Dynamics and control of aggregate thin film surface morphology for improved light trapping: Implementation on a large-lattice kinetic Monte Carlo model , 2011 .
[25] Joseph Sang-Il Kwon,et al. Identification of cell‐to‐cell heterogeneity through systems engineering approaches , 2020 .
[26] H. H. Thodberg,et al. Optimal minimal neural interpretation of spectra , 1992 .
[27] D. Vlachos. Multiscale integration hybrid algorithms for homogeneous–heterogeneous reactors , 1997 .
[28] Luo,et al. Surface roughness and conductivity of thin Ag films. , 1994, Physical review. B, Condensed matter.
[29] Dionisios G. Vlachos,et al. Multiscale model for epitaxial growth of films: Growth mode transition , 2001 .
[30] Sergey Oladyshkin,et al. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion , 2012, Reliab. Eng. Syst. Saf..
[31] D. Vlachos,et al. Catalytic ignition and extinction of hydrogen: comparison of simulations and experiments , 1996 .
[32] Panagiotis D. Christofides,et al. A method for handling batch-to-batch parametric drift using moving horizon estimation: Application to run-to-run MPC of batch crystallization , 2015 .
[33] Charles C. Solvason. Integrated Multiscale Chemical Product Design using Property Clustering and Decomposition Techniques in a Reverse Problem Formulation , 2011 .
[34] J. Kwon,et al. Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester , 2020 .
[35] Panagiotis D. Christofides,et al. Crystal shape and size control using a plug flow crystallization configuration , 2014 .
[36] D. Vlachos. A Review of Multiscale Analysis: Examples from Systems Biology, Materials Engineering, and Other Fluid–Surface Interacting Systems , 2005 .
[37] Panagiotis D. Christofides,et al. Multiscale three-dimensional CFD modeling for PECVD of amorphous silicon thin films , 2018, Comput. Chem. Eng..
[38] Grigoriy Kimaev,et al. A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems , 2017 .
[39] Luis A. Ricardez-Sandoval,et al. Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks , 2019, Chemical Engineering Science.
[40] Venkat Venkatasubramanian,et al. DIAGNOSING NOISY PROCESS DATA USING NEURAL NETWORKS , 1992 .
[41] Antonios Armaou,et al. Control and optimization of multiscale process systems , 2006, Comput. Chem. Eng..
[42] Clarence W. Rowley,et al. A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition , 2014, Journal of Nonlinear Science.
[43] M. Eldred. Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design , 2009 .
[44] Daniel Svozil,et al. Introduction to multi-layer feed-forward neural networks , 1997 .
[45] D. V. Schroeder,et al. An Introduction to Thermal Physics , 2000 .
[46] Maciej Ławryńczuk. Nonlinear predictive control of dynamic systems represented by Wiener–Hammerstein models , 2016 .
[47] Muthanna H. Al-Dahhan,et al. Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors , 2003 .
[48] Thomas A. Adams,et al. Subspace model identification and model predictive control based cost analysis of a semicontinuous distillation process , 2017, Comput. Chem. Eng..
[49] Venkat Venkatasubramanian,et al. A neural network methodology for process fault diagnosis , 1989 .
[50] Joseph Sang-Il Kwon,et al. Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors , 2018, Comput. Chem. Eng..
[51] Jeroen P. van der Sluijs,et al. A framework for dealing with uncertainty due to model structure error , 2004 .
[52] Biao Huang,et al. Output feedback model predictive control for nonlinear systems represented by Hammerstein-Wiener model , 2007 .
[53] Mario R. Eden,et al. Multi-Scale Chemical Product Design using the Reverse Problem Formulation , 2010 .
[54] Frank Allgöwer,et al. Analysis of heterogeneous cell populations: A density-based modeling and identification framework , 2011 .
[55] Fabian J. Theis,et al. Data-driven modelling of biological multi-scale processes , 2015, 1506.06392.
[56] Shabnam Rasoulian,et al. Stochastic nonlinear model predictive control applied to a thin film deposition process under uncertainty , 2016 .
[57] Jack P. C. Kleijnen,et al. Regression and Kriging metamodels with their experimental designs in simulation: A review , 2017, Eur. J. Oper. Res..
[58] Steven L. Brunton,et al. On dynamic mode decomposition: Theory and applications , 2013, 1312.0041.
[59] Frank Allgöwer,et al. Identification of models of heterogeneous cell populations from population snapshot data , 2011, BMC Bioinformatics.
[60] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[61] Jiali Li,et al. Recurrent Neural Network-based Model Predictive Control for Continuous Pharmaceutical Manufacturing , 2018, Mathematics.
[62] A. Lapedes,et al. Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .
[63] Dionisios G. Vlachos,et al. Homogeneous-heterogeneous oxidation reactions over platinum and inert surfaces , 1996 .
[64] Shabnam Rasoulian,et al. Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems , 2016 .
[65] Ahmet Demir,et al. Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors , 2007, Environ. Model. Softw..
[66] Victor Picheny,et al. Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise , 2017, Eur. J. Oper. Res..
[67] Prashant Mhaskar,et al. Subspace-based model identification of a hydrogen plant startup dynamics , 2017, Comput. Chem. Eng..
[68] Noor Quddus,et al. Developing Quantitative Structure–Property Relationship Models To Predict the Upper Flammability Limit Using Machine Learning , 2019, Industrial & Engineering Chemistry Research.
[69] R. Adomaitis. A reduced-basis discretization method for chemical vapor deposition reactor simulation , 2003 .
[70] Grigoriy Kimaev,et al. Multilevel Monte Carlo for noise estimation in stochastic multiscale systems , 2018, Chemical Engineering Research and Design.
[71] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[72] V. Venkatasubramanian. The promise of artificial intelligence in chemical engineering: Is it here, finally? , 2018, AIChE Journal.
[73] Luis A. Ricardez-Sandoval,et al. Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach , 2018, Comput. Chem. Eng..
[74] R. Adomaitis. Multiscale modeling and optimization of an atomic layer deposition process for nanomanufacturing applications , 2010 .
[75] Raymond A. Adomaitis,et al. Development of a multiscale model for an atomic layer deposition process , 2010 .
[76] Panagiotis D. Christofides,et al. Modeling and control of crystal shape in continuous protein crystallization , 2014 .
[77] Panagiotis D. Christofides,et al. Modeling and Control of Protein Crystal Shape and Size in Batch Crystallization , 2013 .
[78] Laurene V. Fausett,et al. Fundamentals Of Neural Networks , 1994 .
[79] Shabnam Rasoulian,et al. Uncertainty analysis and robust optimization of multiscale process systems with application to epitaxial thin film growth , 2014 .