139 lines
10 KiB
BibTeX
139 lines
10 KiB
BibTeX
@article{2020SciPy-NMeth,
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title = {{{SciPy}} 1.0: {{Fundamental}} Algorithms for Scientific Computing in Python},
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author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
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year = 2020,
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journal = {Nature Methods},
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volume = {17},
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pages = {261--272},
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doi = {10.1038/s41592-019-0686-2}
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}
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@misc{anderskvellestadProject4FYS3150,
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title = {Project 4 --- {{FYS3150}}/{{FYS4150}} Course Material},
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author = {{Anders Kvellestad}},
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urldate = {2025-11-19},
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howpublished = {https://anderkve.github.io/FYS3150/book/projects/project4.html},
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file = {/home/lars/Zotero/storage/V5DGD436/project4.html}
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}
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@misc{ErgodicitySpringerLink,
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title = {Ergodicity \textbar{} {{SpringerLink}}},
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urldate = {2025-11-19},
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howpublished = {https://link.springer.com/chapter/10.1007/978-3-662-03961-8\_19},
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file = {/home/lars/Zotero/storage/B8AA6GLC/978-3-662-03961-8_19.html}
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}
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@misc{glatt-holtzParallelMCMCAlgorithms2024,
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title = {Parallel {{MCMC Algorithms}}: {{Theoretical Foundations}}, {{Algorithm Design}}, {{Case Studies}}},
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shorttitle = {Parallel {{MCMC Algorithms}}},
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author = {{Glatt-Holtz}, Nathan E. and Holbrook, Andrew J. and Krometis, Justin A. and Mondaini, Cecilia F.},
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year = 2024,
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month = jul,
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number = {arXiv:2209.04750},
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eprint = {2209.04750},
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primaryclass = {stat},
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publisher = {arXiv},
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doi = {10.48550/arXiv.2209.04750},
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urldate = {2025-11-19},
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abstract = {Parallel Markov Chain Monte Carlo (pMCMC) algorithms generate clouds of proposals at each step to efficiently resolve a target probability distribution. We build a rigorous foundational framework for pMCMC algorithms that situates these methods within a unified 'extended phase space' measure-theoretic formalism. Drawing on our recent work that provides a comprehensive theory for reversible single proposal methods, we herein derive general criteria for multiproposal acceptance mechanisms which yield ergodic chains on general state spaces. Our formulation encompasses a variety of methodologies, including proposal cloud resampling and Hamiltonian methods, while providing a basis for the derivation of novel algorithms. In particular, we obtain a top-down picture for a class of methods arising from 'conditionally independent' proposal structures. As an immediate application, we identify several new algorithms including a multiproposal version of the popular preconditioned Crank-Nicolson (pCN) sampler suitable for high- and infinite-dimensional target measures which are absolutely continuous with respect to a Gaussian base measure. To supplement our theoretical results, we carry out a selection of numerical case studies that evaluate the efficacy of these novel algorithms. First, noting that the true potential of pMCMC algorithms arises from their natural parallelizability, we provide a limited parallelization study using TensorFlow and a graphics processing unit to scale pMCMC algorithms that leverage as many as 100k proposals at each step. Second, we use our multiproposal pCN algorithm (mpCN) to resolve a selection of problems in Bayesian statistical inversion for partial differential equations motivated by fluid measurement. These examples provide preliminary evidence of the efficacy of mpCN for high-dimensional target distributions featuring complex geometries and multimodal structures.},
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archiveprefix = {arXiv},
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keywords = {Mathematics - Probability,Mathematics - Statistics Theory,Statistics - Computation},
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file = {/home/lars/Zotero/storage/9DBGSWDS/Glatt-Holtz et al. - 2024 - Parallel MCMC Algorithms Theoretical Foundations, Algorithm Design, Case Studies.pdf;/home/lars/Zotero/storage/IFPYDLIP/2209.html}
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}
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@article{harrisArrayProgrammingNumPy2020,
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title = {Array Programming with {{NumPy}}},
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author = {Harris, Charles R. and Millman, K. Jarrod and van der Walt, St{\'e}fan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H. and Brett, Matthew and Haldane, Allan and del R{\'i}o, Jaime Fern{\'a}ndez and Wiebe, Mark and Peterson, Pearu and {G{\'e}rard-Marchant}, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
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year = 2020,
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month = sep,
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journal = {Nature},
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volume = {585},
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number = {7825},
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pages = {357--362},
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publisher = {{Springer Science and Business Media LLC}},
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doi = {10.1038/s41586-020-2649-2}
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}
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@article{hunterMatplotlib2DGraphics2007,
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title = {Matplotlib: {{A 2D}} Graphics Environment},
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author = {Hunter, J. D.},
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year = 2007,
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journal = {Computing in Science \& Engineering},
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volume = {9},
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number = {3},
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pages = {90--95},
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publisher = {IEEE COMPUTER SOC},
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doi = {10.1109/MCSE.2007.55},
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abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.}
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}
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@article{kulskeIsingModelHighlights2025,
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title = {The {{Ising}} Model: Highlights and Perspectives},
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shorttitle = {The {{Ising}} Model},
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author = {K{\"u}lske, Christof},
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year = 2025,
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month = aug,
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journal = {Mathematical Physics, Analysis and Geometry},
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volume = {28},
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number = {3},
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pages = {20},
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issn = {1572-9656},
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doi = {10.1007/s11040-025-09515-1},
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urldate = {2025-11-19},
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abstract = {We give a short non-technical introduction to the Ising model, and review some successes as well as challenges which have emerged from its study in probability and mathematical physics. This includes the infinite-volume theory of phase transitions, and ideas like scaling, renormalization group, universality, SLE, and random symmetry breaking in disordered systems and networks. This note is based on a talk given on 15 August 2024, as part of the Ising lecture during the 11th Bernoulli-IMS world congress, Bochum.},
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langid = {english},
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keywords = {60K35,82B20,82B26,Disordered systems,Extremal decomposition,Gibbs measures,Ising model,Phase transitions,Renormalization group,SLE,Spin models},
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file = {/home/lars/Zotero/storage/ZR75W2W7/Külske - 2025 - The Ising model highlights and perspectives.pdf}
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}
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@article{metraTemperaturedependentCriticalityRandom2021,
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title = {Temperature-Dependent Criticality in Random {{2D Ising}} Models},
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author = {Metra, Matteo and Zorrilla, Luc and Zani, Maurizio and Puppin, Ezio and Biscari, Paolo},
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year = 2021,
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month = sep,
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journal = {The European Physical Journal Plus},
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volume = {136},
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number = {9},
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eprint = {2108.13725},
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primaryclass = {cond-mat},
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pages = {939},
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issn = {2190-5444},
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doi = {10.1140/epjp/s13360-021-01939-2},
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urldate = {2025-11-19},
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abstract = {We consider 2D random Ising ferromagnetic models, where quenched disorder is represented either by random local magnetic fields (Random Field Ising Model) or by a random distribution of interaction couplings (Random Bond Ising Model). In both cases we first perform zero- and finite-temperature Monte-Carlo simulations to determine how the critical temperature depends on the disorder parameter. We then focus on the reversal transition triggered by an external field, and study the associated Barkhausen noise. Our main result is that the critical exponents characterizing the power-law associated with the Barkhausen noise exhibit a temperature dependence in line with existing experimental observations.},
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archiveprefix = {arXiv},
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keywords = {Condensed Matter - Disordered Systems and Neural Networks,Condensed Matter - Statistical Mechanics},
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file = {/home/lars/Zotero/storage/HFEXCMHD/Metra et al. - 2021 - Temperature-dependent criticality in random 2D Ising models.pdf;/home/lars/Zotero/storage/2TETXMJX/2108.html}
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}
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@misc{mullickSociophysicsModelsInspired2025,
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title = {Sociophysics Models Inspired by the {{Ising}} Model},
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author = {Mullick, Pratik and Sen, Parongama},
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year = 2025,
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month = jun,
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number = {arXiv:2506.23837},
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eprint = {2506.23837},
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primaryclass = {physics},
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publisher = {arXiv},
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doi = {10.48550/arXiv.2506.23837},
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urldate = {2025-11-19},
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abstract = {The Ising model, originally developed for understanding magnetic phase transitions, has become a cornerstone in the study of collective phenomena across diverse disciplines. In this review, we explore how Ising and Ising-like models have been successfully adapted to sociophysical systems, where binary-state agents mimic human decisions or opinions. By focusing on key areas such as opinion dynamics, financial markets, social segregation, game theory, language evolution, and epidemic spreading, we demonstrate how the models describing these phenomena, inspired by the Ising model, capture essential features of collective behavior, including phase transitions, consensus formation, criticality, and metastability. In particular, we emphasize the role of the dynamical rules of evolution in the different models that often converge back to Ising-like universality. We end by outlining the future directions in sociphysics research, highlighting the continued relevance of the Ising model in the analysis of complex social systems.},
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archiveprefix = {arXiv},
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keywords = {Condensed Matter - Statistical Mechanics,Physics - Computational Physics,Physics - Physics and Society},
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file = {/home/lars/Zotero/storage/FDJZV7PH/Mullick and Sen - 2025 - Sociophysics models inspired by the Ising model.pdf;/home/lars/Zotero/storage/CKDZET55/2506.html}
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}
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@misc{teamPandasdevPandasPandas2025,
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title = {Pandas-Dev/Pandas: {{Pandas}}},
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shorttitle = {Pandas-Dev/Pandas},
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author = {pandas development {team}, The},
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year = 2025,
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month = sep,
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doi = {10.5281/zenodo.17229934},
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urldate = {2025-10-16},
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abstract = {Pandas is a powerful data structures for data analysis, time series, and statistics.},
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howpublished = {Zenodo},
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keywords = {data science,python},
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file = {/home/lars/Zotero/storage/QV289HHN/17229934.html}
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}
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