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At least 1,099 records · Page 61

Is Bayesian inference "brittle"?

In a recent report, “Bayesian Brittleness: Why no Bayesian model is ‘good enough,’” (arXiv, 1304.6772v1) the authors prove rigorously that model variations that are arbitrarily small, in a particular technical sense, the posterior expectation of a function can achieve essentially any value that the function alone can achieve, so that Bayesian inference appears to have no robustness whatsoever. We explain this puzzling result, and show why it does not imply a breakdown of Bayesian inference. The explanation is that the models leading to the extreme results depend on the observed data.

97 MATHEMATICS AND COMPUTING↗

On the Equivalence of Trapped Colloids, Pinned Vortices, and Spin Ice

We investigate the recently reported analogies between pinned vortices in nano-structured superconductors or colloids in optical traps, and spin ice materials. The frustration of the two models, one describing colloids and vortices, the other describing spin ice, differs essentially. However, their effective energetics is made identical by the contribution of an emergent field associated to a topological charge. This equivalence extends to the local low-energy dynamics of the ice manifold, yet breaks down in lattices of mixed coordination, because of topological charge transfer between sub-latices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Overview ID/POV Process

This is a flowchart of the inventory difference/propagation of variance process, with steps such as determine all relevance transactions, group relevant transactions by affected MATLID, filter groups to S, F and identify required M's, etc.

97 MATHEMATICS AND COMPUTING↗

In Situ Visualization for Computational Science

In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to trends in the area of high-performance computing. This paradigm holds great promise in its ability to access increased spatio-temporal resolution and leverage extensive computational power. However, the paradigm is also widely viewed as limiting when it comes to exploration-oriented use cases and further will require visualization systems to become more and more complicated and constrained. Additionally, there are many open research topics within situ visualization. The Dagstuhl seminar 18271 "In Situ Visualization for Computational Science" brought together researchers and practitioners from three communities (computational science, high-performance computing, and scientific visualization) to share interesting findings, to identify lines of open research, and to determine a medium-term research agenda that addresses the most pressing problems. This report summarizes the outcomes and findings of the seminar.

Computational Science↗

Partial Support of Meetings of the Board on Mathematical Sciences and Their Applications

The National Academies of Sciences, Engineering, and Medicine received partial funding for quarterly meetings of and outreach activities by of the Board on Mathematical Sciences and Analytics (BMSA), formerly known as the Board on Mathematical Sciences and Their Applications. The total support from DOE was $240,000 over 36 months covering the period August 1, 2016 through July 31, 2019. Additional support for this activity comes from the National Science Foundation.

97 MATHEMATICS AND COMPUTING↗

A Whirlwind History of Cryptography

This presentation provides an overview of cryptography starting with the Spartan Scytale used extensively during the Peloponnesian wars up through present day. Cryptography and cryptanalysis developed side by side in arms race. Algorithm development stagnated pending cracking. Once a method of crypto was cracked, development took off. Cryptography and cryptanalysis drove developments in math, linguistics, information theory, and quantum theory, and pushed development of the first computers.

97 MATHEMATICS AND COMPUTING↗

An unstructured C-grid type variational formulation for the sea ice dynamics

Historically, B-grid formulations of sea ice dynamics have been dominant because they have matched the grid type used by ocean models. The reason for the grid match is simple – it facilitates penetration of the curl of ice-ocean stress into the deep ocean with minimal numerical diffusivity because sea ice and ocean velocity are co-located. In recent years, as ocean models have increasingly progressed to C-grids, sea ice models have followed suit on quadrilateral meshes, but the implementation of an unstructured C-grid sea ice models is new. We present an unstructured C-grid discretization of the Elastic Viscous Plastic (EVP) rheology, where the velocity unknowns are discretized at the edges of the mesh cells with n-sides, where typically n is greater than or equal to four, rather than at the vertices, as in the B-grid. Our framework of choice is the Model for Prediction Across Scales (MPAS) within E3SM, the climate model of the U.S. Department of Energy, although our approach is general and could be applied to other models as well. While MPAS-Seaice is currently defined on a B-grid, MPAS-Ocean runs on a C-grid, hence interpolation operators are heavily used when coupled simulations are performed. In this work, we describe a mathematical formulation to transition the dynamics of MPAS-Seaice to a C-grid, in order to ultimately facilitate the coupling with MPAS-Ocean and reduce numerical errors associated with this communication. Numerical results are reported to highlight the features of the method.

58 GEOSCIENCES↗

Workshop on Manufacturing and Integration Challenges for Analog and Neuromorphic Computing

The U.S. Department of Energy’s (DOE’s) Advanced Manufacturing Office (AMO) held its third virtual workshop on Semiconductor R&D for Energy Efficiency on August 11-13, 2021. This public workshop – titled “Manufacturing and Integration Challenges for Analog and Neuromorphic Computing” – brought together more than 180 leading scientific and technical experts to identify challenges and opportunities to improve manufacturing capabilities for analog and neuromorphic computing architectures to significantly increase energy efficiency of microelectronic devices. The workshop featured perspectives of researchers from national laboratories, universities, government agencies, and industry with expertise in novel and emerging devices, processes, and metrology and characterization. This workshop is intended to inform an AMO research, development, demonstration, and deployment (RDD&D) plan to significantly reduce semiconductor energy use by 2030. It was co-sponsored by the Semiconductor Research Corporation (SRC) and the DOE Office of Science’s Advanced Scientific Computing Research (ASCR).

97 MATHEMATICS AND COMPUTING↗

Adopting Code Verification Methodology Based on Model Form

Code verification is an essential part of credibility analysis for computational models. It assesses whether the mathematical model is implemented correctly into the code and whether the numerical methods behave consistently, and is done before solution verification and validation. Robust guidance for code verification exists in the literature. However, there is no known, concise guide for selecting the approach based on the model form that also presents an overview of the common elements. This document was written to address this gap as an accessible reference for beginning a code-verification effort.

97 MATHEMATICS AND COMPUTING↗

Adapting Code Verification Methodology to Model Form

Code verification is an essential part of credibility analysis for computational models. It assesses whether the mathematical model is implemented correctly into the code and whether the numerical methods behave consistently, and is done before solution verification and validation. Robust guidance for code verification exists in the literature. However, there is no known, concise guide for selecting the approach based on the model form that also presents an overview of the common elements. This document was written to address this gap as an accessible reference for beginning a code-verification effort.

97 MATHEMATICS AND COMPUTING↗