Article
Physics, Multidisciplinary
Ryo Takakura, Takayuki Miyadera
Summary: The study explores entropic uncertainty relations in generalized probabilistic theories and proves that these relations can be obtained in these theories, showing that the entropic structure in uncertainty relations is not limited to quantum theory.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2021)
Article
Quantum Science & Technology
David Schmid, John H. Selby, Elie Wolfe, Ravi Kunjwal, Robert W. Spekkens
Summary: This study explores the notion of classicality in generalized probabilistic theory (GPT) and introduces the concept of simplex embeddability as a geometric condition for classicality in GPT. In essence, simplex embeddability requires the state space of a GPT to be embeddable in a simplex and its effect space in the dual of that simplex, providing a new intuitive and standalone notion of classicality for GPTs.
Article
Physics, Mathematical
Takayuki Miyadera, Ryo Takakura
Summary: This study aims to investigate whether a behavior similar to the quantum no-programming theorem can be observed in generalized probabilistic theories (GPTs). By generalizing the programming scheme to GPTs, a similar theorem to the quantum no-programming theorem is derived. Furthermore, the study demonstrates the close relationship between programming of reversible dynamics and a curious structure called a quasi-classical structure on the state space. The programming of irreversible dynamics, i.e., channels, in GPTs is also investigated.
JOURNAL OF MATHEMATICAL PHYSICS
(2023)
Article
Automation & Control Systems
Xiaocheng Zhang, Wenchao Xue, Sen Chen, Haitao Fang
Summary: This paper systematically discusses state estimation for systems with multiple uncertain dynamics and measurement biases, introducing the concept of generalized observability. Necessary and sufficient conditions for observability are developed, with the construction of an observer for estimating observable combinations and quantitative analysis of estimation errors for system state vector. Simulation study validates the theoretical results.
SYSTEMS & CONTROL LETTERS
(2021)
Article
Biology
Tommaso Rigon, Amy H. Herring, David B. Dunson
Summary: In this article, a generalized Bayes framework is proposed to bridge loss-based clustering methods and model-based clustering methods. The proposed method allows for quantification of uncertainty in clustering and provides a way to characterize uncertainty in clustering using Gibbs posteriors. Efficient deterministic algorithms and sampling algorithms are developed based on Bregman divergence and pairwise similarities. Furthermore, existing clustering algorithms can be interpreted as generalized Bayes estimators in this framework, allowing for uncertainty quantification.
Article
Engineering, Multidisciplinary
Jonas Nitzler, Jonas Biehler, Niklas Fehn, Phaedon-Stelios Koutsourelakis, Wolfgang A. Wall
Summary: This article presents a generalized formulation of a Bayesian multi-fidelity Monte-Carlo framework that addresses the challenges of high computational cost and high dimensionality in uncertainty quantification. By exploiting lower-fidelity model versions and learning the relationship between high-fidelity models and lower-fidelity models, the curse of dimensionality is circumvented. Despite the limitations of small data and inaccurate information from low-fidelity models, accurate and certifiable estimates for uncertainty quantification can be obtained with significantly fewer high-fidelity model runs.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Optics
John H. Selby, David Schmid, Elie Wolfe, Ana Belen Sainz, Ravi Kunjwal, Robert W. Spekkens
Summary: The formalism of generalized probabilistic theories (GPTs) was developed to characterize conceivable physical theories. The GPT describing a specific physical theory includes all possible processes. This study introduces an accessible GPT fragment to describe the characteristics of a particular experimental setup within a physical theory. Cone equivalence is defined as an equivalence relation between accessible GPT fragments. Several examples are given to demonstrate the use of accessible GPT fragments in experimental scenarios, and it is proven that failures of generalized noncontextuality can be witnessed without incompatibility among measurements or the assumption of freedom of choice.
Article
Multidisciplinary Sciences
Kevin Rennert, Frank Errickson, Brian C. Prest, Lisa Rennels, Richard G. Newell, William Pizer, Cora Kingdon, Jordan Wingenroth, Roger Cooke, Bryan Parthum, David Smith, Kevin Cromar, Delavane Diaz, Frances C. Moore, Ulrich K. Muller, Richard J. Plevin, Adrian E. Raftery, Hana Sevcikova, Hannah Sheets, James H. Stock, Tammy Tan, Mark Watson, Tony E. Wong, David Anthoff
Summary: This study shows that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods can significantly increase the estimates of the social cost of carbon dioxide (SC-CO2). The study's estimates are higher than the current values used in policy evaluation, thereby increasing the expected benefits of greenhouse gas mitigation.
Article
Chemistry, Analytical
Wojciech Toczek, Janusz Smulko
Summary: The article presents the testing methodology and results of examining the probabilistic model of the measurement process using six risk metrics. An undesirable effect of probability distribution distortion at the edges of the measuring range was detected. Guidelines on using the model to obtain consistent risk assessment with Monte Carlo method results are provided.
Article
Engineering, Multidisciplinary
Pawel Majda, Joanna Jastrzebska
Summary: This paper introduces a new method for measuring machine tool stiffness in generalized coordinates, allowing flexibility and universality. The validation of the method shows that reproducibility and method-specific uncertainties contribute more to the budget than the metrological characteristics of the sensors used. It also demonstrates that certain components of complex uncertainty can be omitted without losing estimation accuracy.
Article
Engineering, Civil
Konstantinos N. Anyfantis, Sofia Pantazopoulou, Nikolaos Papanikolaou
Summary: This work proposes a computationally efficient framework based on probabilistic methods, experimental design, and inferential statistics for constructing generalized buckling response surfaces. Finite Element models are used to map the uncertain input variables to the response quantity, and it is shown that surrogate modeling is not applicable for accelerating the problem. The constructed response surfaces are associated with a probability of non-exceedance and can be used in reliability-based analyses and design schemes.
THIN-WALLED STRUCTURES
(2023)
Article
Energy & Fuels
Shaobo Sun, Kui Shan, Shengwei Wang
Summary: This study proposed an online robust sequencing control strategy for chiller plants under low-quality and uncertain flow measurements, which effectively reduced the impacts of flow measurement uncertainties and improved the performance of chiller plants. The uncertainty processing model accurately quantified the measurement uncertainties of water flow rates, leading to significant reductions in root-mean-square error of cooling loads, total switching number of chillers, and cumulative unmet cooling load. The proposed control strategy showed the ability to tolerate flow measurement uncertainties.
Article
Physics, Multidisciplinary
C. Burgos, J-C Cortes, E. Lopez-Navarro, C. M. A. Pinto, Rafael-J Villanueva
Summary: This paper improves a randomized reformulation of a model in Classical Mechanics that includes a generalized derivative. By utilizing stochastic analysis, reliable approximations of the probability density function of the solution are computed, avoiding the limitations of limited statistical punctual information and the Principle of Maximum Entropy. It is proven that these approximations converge to the exact density under mild conditions on the data. Numerical examples are provided to illustrate the theoretical findings.
EUROPEAN PHYSICAL JOURNAL PLUS
(2022)
Article
Astronomy & Astrophysics
Oliver DeWolfe, Kenneth Higginbotham
Summary: In this discussion, the electromagnetic dualization of 1-form to a 2-form in AdS(5) is shown to exchange regular and alternate boundary conditions, thereby gauging the originally global U(1) symmetry in the dual field theory. This method is then applied to a Maxwell-Chern-Simons theory in AdS, resulting in a theory with a modified field strength that holographically realizes a 2-group symmetry. The holographic renormalization is explicitly carried out to verify the results, and the potential generalization to other rank fields in different dimensions is also discussed.
Article
Geochemistry & Geophysics
Saikat Kuili, Ravi S. Jakka
Summary: This research addresses the issue of uncertainty-induced deviations in geotechnical and geophysical applications, specifically in determining seismic site class. The study highlights the importance of considering various sources of uncertainty in estimating the shear wave velocity of soil deposits. The probabilistic approaches used in this study provide insights into handling uncertainty and allow for a more comprehensive evaluation of seismic site class.
PURE AND APPLIED GEOPHYSICS
(2023)
Article
Physics, Multidisciplinary
Ryo Takakura
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2019)
Article
Physics, Multidisciplinary
Ryo Takakura, Takayuki Miyadera
Summary: The study explores entropic uncertainty relations in generalized probabilistic theories and proves that these relations can be obtained in these theories, showing that the entropic structure in uncertainty relations is not limited to quantum theory.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2021)
