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A. Hanuka, C. Emma, T. Maxwell, A. S. Fisher, B. Jacobson, M. J. Hogan, and Z. Huang (2021), “Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics,”
2021
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2021
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2021
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T.-J. Hou,
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D. Huber, O. V. Marchukov, H.-W. Hammer, and A. G. Volosniev (2021), “Morphology of three-body quantum states from machine learning,”
2021
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N. Ismail, and A. Gezerlis (2021), “Machine-learning approach to finite-size effects in systems with strongly interacting fermions,”
2021
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A. Jany,
2021
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2021
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B. Kaspschak, and U.-G. Meißner (2021), “Neural network perturbation theory and its application to the Born series,”
2021
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M. Kekic,
2021
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2021
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M. Kranjčević, B. Riemann, A. Adelmann, and A. Streun (2021), “Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks,”
2021
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M. O. Kuttan, J. Steinheimer, K. Zhou, A. Redelbach, and H. Stoecker (2021), “Deep Learning Based Impact Parameter Determination for the CBM Experiment,”
2021
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2021
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S. Lawrence, and Y. Yamauchi (2021), “Normalizing Flows and the Real-Time Sign Problem,”
2021
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Y. Liu, C. Su, J. Liu, P. Danielewicz, C. Xu, and Z. Ren (2021), “Improved naive Bayesian probability classifier in predictions of nuclear mass,”
2021
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A. Lovell, F. M. Nunes, M. Catacora-Rios, and G. King (2021), “Recent advances in the quantification of uncertainties in reaction theory,”
2021
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2021
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2021
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2021
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J. Mayer, K. Boretzky, C. Douma, E. Hoemann, and A. Zilges (2021), “Classical and machine learning methods for event reconstruction in neuland,”
2021
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S. A. Miskovich, F. Montes, G. P. A. Berg, J. Blackmon, K. A. Chipps, M. Couder, K. Hermansen, A. A. Hood, R. Jain, H. Schatz, M. S. Smith, P. Tsintari, and L. Wagner (2021), “Online Bayesian optimization for beam alignment in the SECAR recoil mass separator,” in
2021
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2021
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2021
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S. Myren, and E. Lawrence (2021), “A comparison of Gaussian processes and neural networks for computer model emulation and calibration,”
2021
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A. Nandi, C. Qu, P. L. Houston, R. Conte, and J. M. Bowman (2021), “
2021
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D. Neudecker, O. Cabellos, A. R. Clark, M. J. Grosskopf, W. Haeck, M. W. Herman, J. Hutchinson, T. Kawano, A. E. Lovell, I. Stetcu, P. Talou, and S. Vander Wiel (2021), “Informing nuclear physics via machine learning methods with differential and integral experiments,”
2021
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W. G. Newton, and G. Crocombe (2021), “Nuclear symmetry energy from neutron skins and pure neutron matter in a Bayesian framework,”
2021
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K. A. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, and P. Stornati (2021), “Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models,”
2021
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G. Nobre, D. Brown, S. Scoville, M. Fucci, S. Ruiz, R. Crawford, A. Coles, and M. Vorabbi (2021), “Expansion of machine-learning method for classifying neutron resonances,”
2021
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D. Odell, C. Brune, and D. Phillips (2021), “How Bayesian methods can improve
2021
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J.-F. Paquet,
2021
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A. Pastore, and M. Carnini (2021), “Extrapolating from neural network models: a cautionary tale,”
2021
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2021
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D. R. Phillips, R. Furnstahl, U. Heinz, T. Maiti, W. Nazarewicz, F. Nunes, M. Plumlee, S. Pratt, M. Pratola, F. Viens, and S. M. Wild (2021), “Get on the BAND wagon: A Bayesian framework for quantifying model uncertainties in nuclear dynamics,”
2021
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Z. Qian,
2021
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C. Y. Qiao, J. C. Pei, Z. A. Wang, Y. Qiang, Y. J. Chen, N. C. Shu, and Z. G. Ge (2021), “Bayesian evaluation of charge yields of fission fragments of
2021
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K. Raghavan, P. Balaprakash, A. Lovato, N. Rocco, and S. M. Wild (2021), “Machine-learning-based inversion of nuclear responses,”
2021
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R. Roussel, and A. Edelen (2021), “Proximal biasing for Bayesian optimization and characterization of physical systems,” in
2021
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E. Rrapaj, and A. Roggero (2021), “Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks,”
2021
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A. Sarkar, and D. Lee (2021), “Self-learning Emulators and Eigenvector Continuation,”
2021
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G. Saxena, P. K. Sharma, and P. Saxena (2021), “Modified empirical formulas and machine learning for
2021
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A. Scheinker, and D. Scheinker (2021), “Extremum seeking for optimal control problems with unknown time-varying systems and unknown objective functions,”
2021
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2021
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M. Shelley, and A. Pastore (2021), “A new mass model for nuclear astrophysics: Crossing 200 keV accuracy,”
2021
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2021
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R. Solli, D. Bazin, M. Hjorth-Jensen, M. P. Kuchera, and R. R. Strauss (2021), “Unsupervised learning for identifying events in active target experiments,”
2021
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2021
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L. Tong, R. He, and S. Yan (2021), “Prediction of neutron-induced fission product yields by a straightforward
2021
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Z.-A. Wang, and J. Pei (2021), “Optimizing multilayer bayesian neural networks for evaluation of fission yields,”
2021
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S. Wesolowski, I. Svensson, A. Ekström, C. Forssén, R. J. Furnstahl, J. A. Melendez, and D. R. Phillips (2021), “Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables,”
2021
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J.-L. Wynen, E. Berkowitz, S. Krieg, T. Luu, and J. Ostmeyer (2021), “Machine learning to alleviate Hubbard-model sign problems,”
2021
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W.-J. Xie, and B.-A. Li (2021), “Bayesian inference of the incompressibility, skewness and kurtosis of nuclear matter from empirical pressures in relativistic heavy-ion collisions,”
2021
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2021
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2021
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E. Yüksel, D. Soydaner, and H. Bahtiyar (2021), “Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron,”
2021
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X. Zhang, and R. J. Furnstahl (2021), “Fast emulation of quantum three-body scattering,”
2021
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Y. Zhang, and Q. Yang (2021), “A survey on multi-task learning,”
2021
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2021
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2021
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D. Q. Adams,
2022
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M. Agostini, G. Benato, J. A. Detwiler, J. Menéndez, and F. Vissani (2022),
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2022
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N. Kunert, P. T. H. Pang, I. Tews, M. W. Coughlin, and T. Dietrich (2022), “Quantifying modeling uncertainties when combining multiple gravitational-wave detections from binary neutron star sources,”
2022
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2022
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2022
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I. Svensson, A. Ekström, and C. Forssén (2022), “Bayesian parameter estimation in chiral effective field theory using the hamiltonian monte carlo method,”
2022
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N. Schunck, Ed. (2019),
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