Browsing Center for Computational Science and Engineering (CCSE) by Title
Now showing items 43-62 of 73
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New overlapping finite elements and their application in the AMORE paradigm
(Massachusetts Institute of Technology, 2020)The finite element method has become a fundamental analysis tool for modern sciences and engineering. Despite the great improvement in theory and application over the past decades, the need for regular conforming meshes ... -
A novel equivalence method for high fidelity hybrid stochastic-deterministic neutron transport simulations
(Massachusetts Institute of Technology, 2020)With ever increasing available computing resources, the traditional nuclear reactor physics computation schemes, that trade off between spatial, angular and energy resolution to achieve low cost highly-tuned simulations, ... -
Nuclear Computations under Uncertainty New methods to infer and propagate nuclear data uncertainty across Monte Carlo simulations
(Massachusetts Institute of Technology, 2021-06)This thesis introduces new methods to efficiently infer and propagate nuclear data uncertainty across Monte Carlo simulations of nuclear technologies. The main contributions come in two areas: 1. novel statistical methods ... -
Numerical approaches for sequential Bayesian optimal experimental design
(Massachusetts Institute of Technology, 2015)Experimental data play a crucial role in developing and refining models of physical systems. Some experiments can be more valuable than others, however. Well-chosen experiments can save substantial resources, and hence ... -
On traffic disruptions : event detection from visual data and Bayesian congestion games
(Massachusetts Institute of Technology, 2019)Road traffic is often subject to random disturbances due to weather, incidents, or special events. Effectively detecting and disseminating information about disturbances is a key goal of modern, "smart" infrastructure. ... -
Parallel, asynchronous ray-tracing for scalable, 3D, full-core method of characteristics neutron transport on unstructured mesh
(Massachusetts Institute of Technology, 2020)One important goal in nuclear reactor core simulations is the computation of detailed 3D power distributions that will enable higher confidence in licensing of next-generation reactors and lifetime extensions/power up-rates ... -
Path planning and adaptive sampling in the coastal ocean
(Massachusetts Institute of Technology, 2016)When humans or robots operate in complex dynamic environments, the planning of paths and the collection of observations are basic, indispensable problems. In the oceanic and atmospheric environments, the concurrent use of ... -
Physics-constrained machine learning strategies for turbulent flows and bubble dynamics
(Massachusetts Institute of Technology, 2020)Machine learning (ML) has in recent years become a sizzling trend in almost every science and engineering discipline. It enables scientists and engineers to make decisions or draw conclusions directly using information ... -
Prediction under uncertainty : from models for marine-terminating glaciers to Bayesian computation
(Massachusetts Institute of Technology, 2018)The polar ice sheets have enormous potential impact on future global mean sea level rise. Recent observations suggest they are losing mass to the ocean at an accelerated rate. Skillful prediction of the ice sheets' future ... -
Prediction, analysis, and learning of advective transport in dynamic fluid flows
(Massachusetts Institute of Technology, 2021)Transport of any material quantity due to background fields, i.e. advective transport, in fluid dynamical systems has been a widely studied problem. It is of crucial importance in classical fluid mechanics, geophysical ... -
Probabilistic modeling and Bayesian inference via triangular transport
(Massachusetts Institute of Technology, 2022-05)Probabilistic modeling and Bayesian inference in non-Gaussian settings are pervasive challenges for science and engineering applications. Transportation of measure provides a principled framework for treating non-Gaussianity ... -
Probabilistic regional ocean predictions : stochastic fields and optimal planning
(Massachusetts Institute of Technology, 2018)The coastal ocean is a prime example of multiscale nonlinear fluid dynamics. Ocean fields in such regions are complex, with multiple spatial and temporal scales and nonstationary heterogeneous statistics. Due to the limited ... -
Provably convergent anisotropic output-based adaptation for continuous finite element discretizations
(Massachusetts Institute of Technology, 2020)The expansion of modern computing power has seen a commensurate rise in the reliance on numerical simulations for engineering and scientific purposes. Output error estimation combined with metric-based mesh adaptivity ... -
Resilient operations of smart electricity networks under security and reliability failures
(Massachusetts Institute of Technology, 2019)Blackouts (or cascading failures) in Electricity Networks (ENs) can result in severe consequences for economic activity, human safety and national security. Recent incidents suggest that risk of blackouts due to cyber-security ... -
Risk Assessment and Optimal Response Strategies for Resilience of Electric Power Infrastructure to Extreme Weather
(Massachusetts Institute of Technology, 2021-06)Extreme weather is an increasingly critical threat to infrastructure systems. This thesis develops a stochastic modeling and decision-making framework for proactive resource allocation and response strategies to improve ... -
Scaling Bayesian optimization for engineering design : lookahead approaches and multifidelity dimension reduction
(Massachusetts Institute of Technology, 2018)The objective functions and constraints that arise in engineering design problems are often non-convex, multi-modal and do not have closed-form expressions. Evaluation of these functions can be expensive, requiring a ... -
A scientific machine learning approach to learning reduced models for nonlinear partial differential equations
(Massachusetts Institute of Technology, 2021)This thesis presents a new scientific machine learning method which learns from data a computationally inexpensive surrogate model for predicting the evolution of a system governed by a time-dependent nonlinear partial ... -
Scientific Machine Learning for Dynamical Systems: Theory and Applications to Fluid Flow and Ocean Ecosystem Modeling
(Massachusetts Institute of Technology, 2022-09)Complex dynamical models are used for prediction in many domains, and are useful to mitigate many of the grand challenges being faced by humanity, such as climate change, food security, and sustainability. However, because ... -
Sea Spray-Mediated Fluxes at Extreme Wind Speeds
(Massachusetts Institute of Technology, 2021-09)Tropical cyclones are complex systems that are challenging to forecast and model. Since tropical cyclones are powered by the warm ocean surface, the accuracy of intensity forecasts depends heavily on the air-sea interaction ... -
Simulating fluid-solid interaction using smoothed particle hydrodynamics method
(Massachusetts Institute of Technology, 2017)The fluid-solid interaction (FSI) is a challenging process for numerical models since it requires accounting for the interactions of deformable materials that are governed by different equations of state. It calls for the ...