Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction von Krasnopolsky, Vladimir M., Fox-Rabinovitz, Michael S.

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Detailed study of the cluster structure of light nuclei in a three-body model von Kukulin, V.I., Krasnopol'sky, V.M., Voronchev, V.T., Sazonov, P.B.

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Detailed study of the cluster structure of light nuclei in a three-body model von Kukulin, V.I., Krasnopol'sky, V.M., Voronchev, V.T., Sazonov, P.B.

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Detailed study of the cluster structure of light nuclei in a three-body model: (II). The spectrum of low-lying states of nuclei with A = 6 von Kukulin, V.I., Krasnopol'sky, V.M., Voronchev, V.T., Sazonov, P.B.

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Theory of Resonances: Principles and Applications von Kukulin, V. I, Krasnopolsky, V. M, Horácek, J

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The NN-potential with forbidden state suggested from a six-quark model with one-pion exchange von Kukulin, V.I., Pomerantsev, V.N., Krasnopol'sky, V.M., Sazonov, P.B.

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The deuteron structure and NN-phase shifts for realistic local superdeep NN-potential with an extra state von Krasnopol'sky, V.M., Kukulin, V.I., Pomerantsev, V.N., Sazonov, P.B.

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Theory of resonance states based on analytical continuation in the coupling constant von Krasnopolsky, V.M., Kukulin, V.I.

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A new method to describe the stripping to unbound states and other reactions with unstable particles von Krasnopol'sky, V.M., Kukulin, V.I.

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Detailed study of the cluster structure of light nuclei in a three-body model: (I). Ground state of 6Li von Kukulin, V.I., Krasnopol'sky, V.M., Voronchev, V.T., Sazonov, P.B.

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Energy-dependent phase-shift analysis of sup 2 H+ sup 4 He scattering in the energy range 0. 87 lt E sub d lt 5. 24 MeV von Krasnopol'sky, V.M., Kukulin, V.I., Kuznetsova, E.V., Horacek, J., Queen, N.M.

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Artificial neural networks in environmental sciences. I. NNs in satellite remote sensing and satellite meteorology von Krasnopolsky, V.M.

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Artificial neural networks in environmental sciences. II. NNs for fast parameterization of physics in numerical models von Krasnopolsky, V.M.

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How to deal with inhomogeneous outputs and high dimensionality of neural network emulations of model physics in numerical climate and weather prediction models von Krasnopolsky, V.M., Lord, S.J., Moorthi, S., Spindler, T.

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Using adaptive learning techniques for fast and accurate approximation of physics in numerical models von Krasnopolsky, V.M., Fox-Rabinovitz, M., Chalikov, D.

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Complex hybrid models combining deterministic and machine learning components as a new synergetic paradigm in numerical climate modeling and weather prediction von Krasnopolsky, V.M., Fox-Rabinovitz, M.S.

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Robustness of the NN Approach to emulating atmospheric long wave radiation in complex climate models von Krasnopolsky, V.M., Fox-Rabinovitz, M.S., Ming-Dah Chou

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The padé approximant method for finding the Hilbert-Schmidt eigenvalues and eigenfunctions in two- and three-body problems von Krasnopolsky, V.M., Kukulin, V.I.

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Special session panel discussion: methodological issues in the application of learning methods to climate modeling and Earth sciences von Cherkassky, V., Krasnopolsky, V.M., Solomatine, D.P., Valdes, J.J.

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Surface winds from the SSM/I using neural networks von Krasnopolsky, V.M., Breaker, L.C., Gemmill, W.H.

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