Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computational Engineering (2)
- Computer Sciences (2)
- Dynamic Systems (2)
- Engineering (2)
- Mechanical Engineering (2)
-
- Non-linear Dynamics (2)
- Numerical Analysis and Scientific Computing (2)
- Physics (2)
- Statistics and Probability (2)
- Theory and Algorithms (2)
- Analysis (1)
- Artificial Intelligence and Robotics (1)
- Biological and Chemical Physics (1)
- Biostatistics (1)
- Chemistry (1)
- Civil Engineering (1)
- Civil and Environmental Engineering (1)
- Computer Engineering (1)
- Computer and Systems Architecture (1)
- Computer-Aided Engineering and Design (1)
- Control Theory (1)
- Data Science (1)
- Databases and Information Systems (1)
- Design of Experiments and Sample Surveys (1)
- Engineering Mechanics (1)
- Engineering Physics (1)
- Keyword
-
- Biophysics (1)
- Black-box Variational Inference (1)
- Digital twin (1)
- Gaussian Process and Surrogate Model (1)
- Generalization and Robustness (1)
-
- Geometric Analysis (1)
- Geometric Complexity-Minimum Description Length (1)
- Geometry (1)
- Inverse problem and Uncertainty Quantification (1)
- Large Deviations (1)
- Machine learning-based Data and model driven (1)
- Mechanics (1)
- Modeling (1)
- Multiphysics modeling (1)
- Probability (1)
- Simulation (1)
- Stochastic Processes (1)
- Systems (1)
- Systems engineering (1)
Articles 1 - 3 of 3
Full-Text Articles in Dynamical Systems
Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris
Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris
All Dissertations
The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …
Aspects Of Stochastic Geometric Mechanics In Molecular Biophysics, David Frost
Aspects Of Stochastic Geometric Mechanics In Molecular Biophysics, David Frost
All Dissertations
In confocal single-molecule FRET experiments, the joint distribution of FRET efficiency and donor lifetime distribution can reveal underlying molecular conformational dynamics via deviation from their theoretical Forster relationship. This shift is referred to as a dynamic shift. In this study, we investigate the influence of the free energy landscape in protein conformational dynamics on the dynamic shift by simulation of the associated continuum reaction coordinate Langevin dynamics, yielding a deeper understanding of the dynamic and structural information in the joint FRET efficiency and donor lifetime distribution. We develop novel Langevin models for the dye linker dynamics, including rotational dynamics, based …
Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin
All Dissertations
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 …