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Results for “mathematical physics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The Local Equilibrium State of a Crystal Surface Jump Process in the Rough Scaling Regime
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Conglomerate Multi-fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions
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Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement
This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional weighted inner product space. We demonstrate the benefit of working on a weighted space by establishing operator-norm bounds for finite element and graph-based discretizations of Matérn-type priors and deconvolution forward models. For linear-Gaussian inverse problems, we develop a general theory to characterize the error in the approximation to the posterior. We also embed the computational framework into ensemble Kalman methods and MAP estimators for nonlinear inverse problems. Furthermore, our operator-norm bounds for prior discretizations guarantee the scalability and accuracy of these algorithms under mesh refinement.
Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference
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Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE
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Bug fixes / enhancements to Parallel tempering Markov chain Monte Carlo sampler (PTMCMCSampler) v3
Markov chain Monte Carlo sampler useful for a variety of problems for optimization, numerical integration, and generating draws from a probability distribution. They are often used in physical and mathematical problems, especially when it is difficult or impossible to use other approaches. This disclosure is for new bug fixes and enhancements to an existing open source library that is already in the public domain under the MIT license. https://github.com/jellis18/PTMCMCSampler
Better together: Elements of successful scientific software development in a distributed collaborative community
Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities