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Full-Text Articles in Physics
Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin
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Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …
Protein Nano-Object Integrator: Generating Atomic-Style Objects For Use In Molecular Biophysics, Nicholas Smith
Protein Nano-Object Integrator: Generating Atomic-Style Objects For Use In Molecular Biophysics, Nicholas Smith
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As researchers obtain access to greater and greater amounts of computational power, focus has shifted towards modeling macroscopic objects while still maintaining atomic-level details. The Protein Nano-Object Integrator (ProNOI) presented here has been designed to provide a streamlined solution for creating and designing macro-scale objects with atomic-level details to be used in molecular simulations and tools. To accomplish this, two different interfaces were developed: a Protein Data Bank (PDB), PDB-focused interface for generating regularly-shaped three-dimensional atomic objects and a 2D image-based interface for tracing images with irregularly shaped objects and then extracting three-dimensional models from these images. Each interface is …