A5087612414- Magnetic confinement fusion research
- Fusion materials and technologies
- Nuclear reactor physics and engineering
- Markov Chains and Monte Carlo Methods
- COVID-19 and Mental Health
- Ionosphere and magnetosphere dynamics
- Nuclear Physics and Applications
- Statistical Methods and Inference
- COVID-19 Digital Contact Tracing
- Bayesian Methods and Mixture Models
- COVID-19 epidemiological studies
- Gaussian Processes and Bayesian Inference
- Microgrid Control and Optimization
- Particle accelerators and beam dynamics
- Smart Grid Energy Management
- Laser-Plasma Interactions and Diagnostics
- Statistical Mechanics and Entropy
- Scientific Measurement and Uncertainty Evaluation
- Solar and Space Plasma Dynamics
- Superconducting Materials and Applications
- Energy Harvesting in Wireless Networks
Google (United States)
2016-2021
Abstract Contact tracing is increasingly used to combat COVID-19, and digital implementations are now being deployed, many based on Apple Google’s Exposure Notification System. These systems utilize non-traditional smartphone-based technology, presenting challenges in understanding possible outcomes. In this work, we create individual-based models of three Washington state counties explore how exposure notifications combined with other non-pharmaceutical interventions influence COVID-19...
Abstract Contact tracing is increasingly being used to combat COVID-19, and digital implementations are now deployed, many of them based on Apple Google’s Exposure Notification System. These systems new smartphone technology that has not traditionally been for this purpose, presenting challenges in understanding possible outcomes. In work, we use individual-based computational models explore how exposure notifications can be conjunction with non-pharmaceutical interventions, such as...
Many fields of basic and applied science require efficiently exploring complex systems with high dimensionality. An example such a challenge is optimising the performance plasma fusion experiments. The highly-nonlinear temporally-varying interaction between plasma, its environment external controls presents considerable complexity in these A further difficulty arises from fact that there no single objective metric fully captures both quality equipment constraints. To optimise system, we...
The C-2W device (“Norman”) [Gota et al., Nucl. Fusion 59, 112009 (2019)] has produced and sustained beam-driven field-reversed configuration (FRC) plasmas embedded in a magnetic mirror geometry using neutral beams end-bias electrodes located expander divertors. Several discrete vessels comprise this device, many imaging instruments are required order to view the plasma throughout. To meet need, suite of spatially radiometrically calibrated, high-speed camera systems have been deployed....
The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous densities. Favorable scaling with dimension has led to wide adoption of HMC by the statistics community. Modern autodifferentiating software should allow more widespread usage in Bayesian inverse problems. This paper analyzes two major difficulties encountered using for problems: poor conditioning and multimodality. Novel results on preconditioning replica exchange parameter selection are presented context spectroscopy....
We determined the time-dependent geometry, including high-frequency oscillations, of plasma density in TAE's C-2W experiment [Gota et al., Nucl. Fusion 59, 112009 (2019)]. This was done as a joint Bayesian reconstruction from 14-chord FIR interferometer midplane, 32 Mirnov probes at periphery, and 8 shine-through detectors targets neutral beams. For each point time, we recovered, with credibility intervals, radial profile plasma; bulk displacement; amplitudes, frequencies, phases azimuthal...
We propose Powernet as an end-to-end open source technology for economically efficient, scalable and secure coordination of grid resources. It offers integrated hardware software solutions that are judiciously divided between local embedded sensing, computing control, which networked with cloud-based high-level real-time optimal operations not only centralized but also millions distributed resources various types. Our goal is to enable penetration 50% or higher intermittent renewables while...
Hamiltonian Monte Carlo is a popular sampling technique for smooth target densities. The scale lengths of the have long been known to influence integration error and efficiency. However, quantitative measures intrinsic lacking. In this paper, we restrict attention multivariate Gaussian leapfrog integrator, obtain condition number corresponding This number, based on spectral Schatten norms, quantifies steps needed efficiently sample. We demonstrate its utility by using analyze HMC...
The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous densities. Favorable scaling with dimension has led to wide adoption of HMC by the statistics community. Modern auto-differentiating software should allow more widespread usage in Bayesian inverse problems. This paper analyzes two major difficulties encountered using for problems: poor conditioning and multi-modality. Novel results on preconditioning replica exchange parameter selection are presented context...