Browsing Center for Computational Science and Engineering (CCSE) by Title
Now showing items 33-52 of 73
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Learning Mixed Multinomial Logit Models
(Massachusetts Institute of Technology, 2022-09)Multinomial logit (MNL) model is widely used to predict the probabilities of different outcomes. However, standard MNL model suffers from several issues, including but not limited to heterogeneous population, the restricted ... -
Leveraging the Linear Response Theory in Sensitivity Analysis of Chaotic Dynamical Systems and Turbulent Flows
(Massachusetts Institute of Technology, 2023-06)The linear response theory (LRT) provides a set of powerful mathematical tools for the analysis of system’s reactions to controllable perturbation. In applied sciences, LRT is particularly useful in approximating parametric ... -
Mathematical and Computational Foundations to Enable Predictive Digital Twins at Scale
(Massachusetts Institute of Technology, 2021-06)A digital twin is a computational model that evolves over time to persistently represent a unique physical asset. Digital twins underpin intelligent automation by enabling asset-specific analysis and data-driven decision-making. ... -
Mathematical and Computational Modeling of Injection-induced Seismicity
(Massachusetts Institute of Technology, 2023-02)It has long been recognized that pumping fluids into or out of the Earth has the potential to cause earthquakes. Some of the earliest field evidence dates to the 1960s, when earthquakes were turned on and off by water ... -
Model order reduction methods for data assimilation : state estimation and structural health monitoring
(Massachusetts Institute of Technology, 2017)The objective of this thesis is to develop and analyze model order reduction approaches for the efficient integration of parametrized mathematical models and experimental measurements. Model Order Reduction (MOR) techniques ... -
Modeling Feedback Effects of Transient Nuclear Systems Using Monte Carlo
(Massachusetts Institute of Technology, 2023-06)Monte Carlo neutron transport is the gold standard for accurate neutronics simulation of nuclear reactors in steady-state because each term of the neutron transport equation can be directly tallied using continuous-energy ... -
Modeling of piston pin lubrication in internal combustion engines
(Massachusetts Institute of Technology, 2020)The piston pin joins the piston and the connecting rod to transfer the linear force on the piston to rotate the crankshaft that is the eventual power outlet of the engine. The interfaces between the piston pin and the pin ... -
A molecular dynamics study of the tribological properties of diamond like carbon
(Massachusetts Institute of Technology, 2020)Diamond like carbon (DLC) is an attractive choice as a coating for mechanical components, because of its excellent wear resistance and very low coefficient of friction . We use molecular dynamics (MD) simulations with a ... -
A Monte Carlo framework for nuclear data uncertainty propagation via the windowed multipole formalism
(Massachusetts Institute of Technology, 2020)A new framework has been developed that calculates the uncertainty in calculated quantities, such as K[subscript eff], reactivity coefficients, multigroup cross sections, and reaction rate ratios, that arise due to ... -
Multi-agent real-time decision making in water resources systems
(Massachusetts Institute of Technology, 2018)Optimal utilization of natural resources such as water, wind and land over extended periods of time requires a carefully designed framework coupling decision making and a mathematical abstraction of the physical system. ... -
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 ...