Barros, V. et al. (Eds.) Climate change impacts, adaptation and vulnerability in 2014 (Cambridge University Press, 2014).
Wigley, P. et al. Fast machine learning online optimization of ultracold atom experiments. Science.Member of Parliament 625890 (2016).
Scheinker, A. & Gessner, S. Adaptive method for electron bunch profile prediction. Physics. Pastor Axel.beam 18102801 (2015).
Noack, M. et al. A Kriging-based approach to autonomous experiments with application to X-ray scattering. Science.Member of Parliament 911809 (2019).
Coveney, PV, Boon, JP & Succi, S. Bridging the gap at the interface of physics, chemistry, and biology. Philos. Trans. R. Soc. Lond. Sir. A 37420160335 (2016).
Google Scholar
Hu, SX et al. First-principles thermal conductivity of warm, dense deuterium plasmas in inertial confinement fusion applications. Physics. Rev.E 89043105 (2014).
Stanton, LG, Glosli, JN & Murillo, MS Multiscale molecular dynamics models of heterogeneously charged systems. Physics. Rev.X 8021044 (2018).
Google Scholar
Brown, EW, Clark, BK, DuBois, JL, Ceperley, DM Path integral Monte Carlo simulation of a warm, dense, homogeneous electron gas. Physics. Pastor Rhett. 110146405 (2013).
Schmidt, J., Marques, M., Botti, S., Marques, M. Recent advances and applications of machine learning in solid-state materials science. NPJ Computing. meter. Five83 (2019).
Liu, Y., Zhao, T., Ju, W., Shi, S. Materials discovery and design using machine learning. J. Materialomics 3159–177 (2017).
Lubbers, N. et al. Modeling and scale bridging using machine learning: Nanoconfinement effects in porous media. Science.Member of Parliament Ten13312 (2020).
Diaw, A. et al. Multiscale simulation of plasma flow using active learning. Physics. Rev.E 102023310 (2020).
Roehm, D. et al. Distributed Database Kriging for Adaptive Sampling (D2 dregs). Calculate. Physics. common. 192138–147 (2015).
Coulomb, J.-L., Kobetski, A., Caldora Costa, M., Maréchal, Y., Jonsson, U. Comparison of radial basis function approximation methods. COMPEL – Internal J. Compute. Math. Electrical. electronic.engineering twenty two616–629 (2003).
Wu, Y., Wang, H., Zhang, B., Du, K.-L. Using radial basis function networks for function approximation and classification. ISRN application. Math. year 2012324194 (2012).
Park, J. & Sandberg, IW Universal approximation using radial basis function networks. neural computing. 3246–257 (1991).
McKerns, M., Hung, P. & Aivazis, M. mystic: highly constrained nonconvex optimization and UQ. PyPI http://pypi.python.org/pypi/mystic (2009).
McKerns, M., Strand, L., Sullivan, TJ, Fang, A. & Aivazis, M. Building a framework for predictive science.in Procedures 10th Python in Science Conference (Eds. S. van der Walt and J. Millman) 67–78 (SciPy, 2011).
Rastrizine, Louisiana external control system (Mir, 1974) [in Russian].
Rosenbrock, H. An automatic method for finding the maximum or minimum value of a function. Calculate. J. 3175–184 (1960).
Dixon, L. & Zego, G. Towards overall optimization 2 (Dixon, LC, Sego, GP eds.) 1–15 (North Holland, 1978).
Mihalewicz, Z. Genetic algorithm + data structure = evolutionary program (Springer-Verlag, 1992).
Iasum, E. Survey of global optimization techniques. Master of Engineering Dissertation University of Louisville (1990).
Lonardoni, D., Tews, I., Gandolfi, S. & Carlson, J. Nuclear and neutron star matter from local chiral interactions. Physics. Rev.Res. 2022033 (2020).
Annala, E., Gorda, T., Kurkela, A., Nättilä, J. & Vuorinen, A. Evidence for quark matter cores in massive neutron stars. nut. Physics. 16907–910 (2020).
Baym, G. et al. From hadrons to quarks in neutron stars: a review. Progressive member. Physics. 81056902 (2018).
Adam, J. et al. Nonmonotonic energy dependence of net proton number fluctuations. Physics. Pastor Rhett. 126092301 (2021).
Busza, W., Rajagopal, K. & van der Schee, W. Heavy ion collisions: The big picture and big questions. Anne. Pastor Nucl. part. Science. 68339–376 (2018).
Braun-Munzinger, P., Koch, V., Schafer, T. & Stachel, J. Properties of hot and dense materials by relativistic heavy ion collisions. Physics.Member of Parliament 62176–126 (2016).
Raaijmakers, G. et al. Constraints on the equation of state of dense matter and the properties of neutron stars obtained from the mass radius estimation of psr j0740+6620 and multimessenger observations by nicer. Astrophy. J.Lett. 918L29 (2021).
Capano, CD et al. Strict constraints on the radius of neutron stars based on multi-messenger observations and nuclear theory. nut. Astron. Four625–632 (2020).
Dietrich, T. et al. Multimessenger constraints on the equation of state and Hubble constant for neutron stars. science 3701450–1453 (2020).
Reilly, TE et al psr j0030+0451 easy to understand diagram: millisecond pulsar parameter estimation. Astrophy. J. 887L21 (2019).
Miller, M.C. et al. The mass and radius of Psr j0030+0451 come from better data and effects on the properties of neutron star material. Astrophy. J. 887L24 (2019).
Dexheimer, V. Tabular equation of state for a neutron star modeled within a chiral mean-field model. Publication. Astron. Society August. https://doi.org/10.1017/pasa.2017.61 (2017).
Abbott, B. et al. Gw170817: Observation of gravitational waves from a binary neutron star. Physics. Pastor Rhett. 119161101 (2017).
Typel, S., Oertel, M. & Klähn, T. CompOSE Reconciling nuclear physics and astrophysics CompStar online supernova equation of state compose.obspm.fr. Physics. part. Nucl. 46633–664 (2015).
Schneider, AS, Constantinou, C., Muccioli, B. & Prakash, M. Akmal–Pandaripande–Ravenhall equation of state for simulations of supernovae, neutron stars, and binary star mergers. Physics. Rev.C 100025803 (2019).
Raithel, CA, Özel, F. & Psaltis, D. Finite temperature extension of the equation of state for cold neutron stars. Astrophy. J. 87512 (2019).
glendenning, north carolina Compact stars: nuclear physics, particle physics, general relativity (Springer, 1997).
Hempel, M., Pagliara, G. & Schaffner-Bielich, J. Conditions for phase equilibrium in supernovae, protoneutrons, and neutron stars. Physics. Rev.D 80125014 (2009).
Fisher, T. et al. A nuclear collapse supernova explosion caused by a quark-hadron phase transition in the early stages after the bounce. Astrophy. J. Suppl. Sir. 19439 (2011).
McClellan L. & Reddy S. Quachonic matter and neutron stars. Physics. Pastor Rhett. 122122701 (2019).
Chodos, A., Jaffe, RL, Johnson, K., Thorn, CB & Weisskopf, VF A new extended model of hadrons. Physics. Rev.D 93471–3495 (1974).
Schertler, K., Greiner, C., Schaffner-Bielich, J. & Thoma, MH Quark phase in neutron stars and the third family of compact stars as a feature of phase transitions. Nucl. Physics.a 677463–490 (2000).
Rocha, H. On the selection of the most appropriate radial basis functions. applied mathematics. model. 331573–1583 (2009).
Rummelhart, DE, Hinton, GA, and Williams, Joseph. Parallel distributed processing: Exploring the fine structure of cognition (DE, Rumelhart, JL, McClelland, and PDP Research Group (eds.) (MIT Press, 1987).
Schaback, R. & Wendland, H. Adaptive greedy techniques for approximate solutions of large-scale RBF systems. numbers.algorithm twenty four239–254 (2000).
Dorvlo, AS, Jervase, JA & Al-Lawati, A. Solar radiation estimation using artificial neural networks. applied energy 71307–319 (2002).
Diaw, A., McKerns, M., Sagert, I., Stanton, LG, Murillo, MS Directed Sampling Dataset. Zenodo https://doi.org/10.5281/zenodo.10908462 (2024).
Diaw, A., McKerns, M., Sagert, I., Stanton, LG & Murillo, MS Efficient learning of accurate surrogates for simulation of complex systems. code ocean https://doi.org/10.24433/CO.1152070.v1 (2024).
