Abstract
This article surveys modeling and prediction of various nuclidic properties with feedforward artificial neural networks. Special emphasis is placed on neural network modeling of nuclear ground state masses, the cleanprop algorithm for training neural networks on data with error bars, global neural network models of nuclear stability and branching ratios of decay of unstable nuclides, and higher-order probabilistic perceptrons for classifying nuclides as stable or unstable. The various network architectures and training algorithms devised for and successfully tested in these applications are discussed in detail and the best of numerical neural network results are presented.
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Athanassopoulos, S., Mavrommatis, E., Gernoth, K. A., Clark, J. W. (1998): To be published.
Clark, J. W., Gazula, S., Gernoth, K. A., Hasenbein, J., Prater, J. S., Bohr, H. (1992): Collective Computation of Many-Body Properties by Neural Networks. Recent Progress in Many-Body Theories, Vol. 3, edited by Ainsworth, T. L., Campbell, C. E., Clements, B. E., Krotscheck, E. (Plenum Press, New York), 371–386.
Clark, J. W., Gernoth, K. A. (1992): Teaching Neural Networks to do Science. Structure: From Physics to General Systems, Vol. 2, edited by Marinaro, M., Scarpetta, G. (World Scientific, Singapore), 64–77.
Clark, J. W., Gernoth, K. A. (1995): Statistical Modeling of Nuclear Masses Using Neural Network Algorithms. Condensed Matter Theories, Vol. 10, edited by Casas, M., de Llano, M., Navarro, J., Polls, A. (Nova Science Publishers, Commack, NY), 317–333.
Clark, J. W., Gernoth, K. A., Dittmar, S., Ristig, M. L. (1999): Higher-Order Probabilistic Perceptrons as Bayesian Inference Engines. To be published.
Clark, J. W., Gernoth, K. A., Ristig, M. L. (1994): Connectionist Many-Body Phenomenology. Condensed Matter Theories, Vol. 9, edited by Clark, J. W., Shoaib, K. A., Sadiq, A. (Nova Science Publishers, Commack, NY), 519–537.
Clark, J. W., Gernoth, K. A., Ristig, M. L. (1995): Connectionist Statistical Inference. Recent Progress in Many-Body Theories, Vol. 4, edited by Schachinger, E., Mitter, H., Sormann, H. (Plenum Press, New York), 283–292.
Duda, R. O., Hart, P. E. (1973): Pattern Classification and Scene Analysis (Wiley, New York).
Gazula, S., Clark, J. W., Bohr, H. (1992): Learning and Prediction of Nuclear Stability by Neural Networks. Nucl. Phys. A 540, 1–26.
Gernoth, K. A., Clark, J. W. (1995a): Neural Networks that Learn to Predict Probabilities: Global Models of Nuclear Stability and Decay. Neural Networks 8, 291–311.
Gernoth, K. A., Clark, J. W. (1995b): A Modified Backpropagation Algorithm for Training Neural Networks on Data with Error Bars. Comput. Phys. Commun. 88, 1–22.
Gernoth, K. A., Clark, J. W. (1995c): Neural Network Models of Nuclear and Noisy Data. New Computing Techniques in Physics Research IV, edited by Denby, B., Perret-Galix, D. (World Scientific, Singapore), 425–430.
Gernoth, K. A., Clark, J. W., Prater, J. S., Bohr, H. (1993): Neural Network Models of Nuclear Systematics. Phys. Lett. B 300, 1–7.
Hertz, J., Krogh, A., Palmer, R. G. (1991): Introduction to the Theory of Neural Computation (Addison-Wesley, Redwood City, CA).
Kalman, B. L. (1994): Private Communication to Clark, J. W., Washington University, St. Louis, MO, USA.
Kullback, S. (1959): Information Theory and Statistics (Wiley, New York).
Lönnblad, L., Peterson, C., Pi, H., Rögnvaldsson (1991): Self-Organizing Networks for Extracting Jet Features. Comput. Phys. Commun. 67, 193–209.
Masson, P. J., Jänecke, J. (1988): Masses from an Inhomogeneous Partial Difference Equation with Higher-Order Isospin Contributions. At. Data Nucl. Data Tables 39, 273–280.
Mathews, B. W. (1975): Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme. Biochim. Biophys. Acta 405, 442–451.
Mathews, J., Walker, J. L. (1970): Mathematical Methods of Physics (Benjamin, New York).
Navrommatis, E., Dakos, A., Gernoth, K. A., Clark, J. W. (1998): Calculations of Nuclear Half-Lives with Neural Nets. Condensed Matter Theories, Vol. 13, edited by da Providência, J., Malik, F. B. (Nova Science Publishers, Commack, NY), in press.
Minsky, H., Papert, S. (1969): Perceptrons (MIT Press, Cambridge, MA).
Möller, P., Nix, J. R. (1981): Nuclear Mass Formula with a Yukawa-plus-Exponential Macroscopic Model in a Folded-Yukawa Single-Particle Potential. Nucl. Phys. A 361, 117–146.
Möller, P., Nix, J. R. (1988): Nuclear Masses from a Unified Macroscopic-Microscopic Model. At. Data Nucl. Data Tables 39, 213–223.
Möller, P., Nix, J. R. (1994): Stability of Heavy and Superheavy Elements. J. Phys. G 20, 1681–1747.
Möller, P., Nix, J. R., Myers, W. D., Swiatecki, W. J. (1992): The Coulomb Redistribution Energy as Revealed by a Refined Study of Nuclear Masses. Nucl. Phys. A 536, 61–71.
Müller, B., Reinhardt, J., Strickland, M. T. (1995): Neural Networks (Springer-Verlag, Berlin).
Myers, W. D., Swiatecki, W. J. (1966): Nuclear Masses and Deformations. Nucl. Phys. 81, 1–60.
Qian, M., Gong, G., Clark, J. W. (1991): Relative Entropy and Learning Rules. Phys. Rev. A 43, 1061–1070.
Rummelhart, D. E., McClelland, J. L., and the PDP Research Group (1986): Explorations in the Microstructure of Cognition, Vols. 1 and 2 (MIT Press, Cambridge, MA).
Stolorz, P., Lapedes, A., Xia, Y. (1991): Predicting Protein Secondary Structure Using Neural Net and Statistical Methods. J. Molec. Biol. 225, 363–377.
von Mises, R. (1964): Mathematical Theory of Probability and Statistics (Academic Press, New York).
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Gernoth, K.A. (1999). Nuclear physics with neural networks. In: Clark, J.W., Lindenau, T., Ristig, M.L. (eds) Scientific Applications of Neural Nets. Lecture Notes in Physics, vol 522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0104279
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DOI: https://doi.org/10.1007/BFb0104279
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