This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Lionel Raff is Regents Professor in the Department of Chemistry at Oklahoma State University. Ranga Komanduri is Professor & A. H. Nelson, Jr. Endowed Chair in Engineering in the School of Mechanical and Aerospace Engineering at Oklahoma State University. Martin Hagan is Professor in the School of Electrical and Computer Engineering, Oklahoma State University Satish Bukkapatnam is Assistant Professor in the School of Industrial Engineering and Management at Oklahoma State University.
Inhaltsangabe
* Preface * Acronyms * Chapter 1: Fitting Potential-Energy Hypersurfaces * Chapter 2: Overview of Some Non-NN Methods for Fitting Ab Initio Potential Energy Databases * Chapter 3: Feed-forward Neural Networks * Chapter 4: Configuration Space Sampling Methods * Chapter 5: Applications of NN Fitting of Potential-Energy Surfaces * Chapter 6: Potential Surfaces Using Expansion Methods and Neural Networks * Chapter 7: Genetic Algorithm (GA) and Internal Energy Transfer Calculations using NN Methods * Chapter 8: Empirical PES Fitting Using Feed-forward Neural Networks * Chapter 9: NN Methods for Data Analysis and Statistical Error Reduction * Chapter 10: Other Applications of NNs to Quantum Mechanical Problems * Chapter 11: Summary, Conclusions, and Future Trends * References * Subject Index
* Preface * Acronyms * Chapter 1: Fitting Potential-Energy Hypersurfaces * Chapter 2: Overview of Some Non-NN Methods for Fitting Ab Initio Potential Energy Databases * Chapter 3: Feed-forward Neural Networks * Chapter 4: Configuration Space Sampling Methods * Chapter 5: Applications of NN Fitting of Potential-Energy Surfaces * Chapter 6: Potential Surfaces Using Expansion Methods and Neural Networks * Chapter 7: Genetic Algorithm (GA) and Internal Energy Transfer Calculations using NN Methods * Chapter 8: Empirical PES Fitting Using Feed-forward Neural Networks * Chapter 9: NN Methods for Data Analysis and Statistical Error Reduction * Chapter 10: Other Applications of NNs to Quantum Mechanical Problems * Chapter 11: Summary, Conclusions, and Future Trends * References * Subject Index
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