Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 85-86). / The Large Hadron Collider (LHC) is the world's largest and highest energy particle accelerator. With the particle collisions produced at the LHC and measured with the Compact Muon Solenoid (CMS) detector, the CMS experimental group performs precision measurements and general searches for new physics. Year-round CMS operations produce 100 Petabytes of physics data per year, which is stored within a globally distributed grid network of 70 scientific institutions. By 2027, upgrades to the LHC and CMS detector will allow unprecedented probes of microscopic physics, but in doing so generate 2,000 Petabytes (2 Exabytes) of physics data per year. To address the computational requirements of CMS, the cost of CPU resources, disk and tape storage, and tape drives were modeled. These resources were then used in a model of the major CMS computing processes and required infrastructure. / In addition to estimating budget requirements, this model produced bandwidth requirements, for which the transatlantic network cable was explicitly addressed. Given discrete or continuously parameterized policy decisions, the system cost and required network bandwidth could be modeled as a function of the policy. This sensitivity analysis was coupled to an uncertainty quantification of the model outputs, which were functions of the estimated system parameters. The expected value of the system cost and maximum transatlantic network activity were modeled to increase 40 times in 2027 relative to 2018. In 2027 the required transatlantic network capacity was modeled to have an expected value of 210 Gbps, with a 95% confidence interval that reaches 330 Gbps, just under the current bandwidth of 340 Gbps. By changing specific computing policies, the system cost and network load were shown to decrease. / Specific policies can reduce the network load to an expected value of 150 Gbps, with a 95% confidence interval that reaches 260 Gbps. Given the unprecedented volume of data, such policy changes can allow CMS to meet its future physics goals. / by Evan K. Massaro. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 103-116). / Dengue fever is currently the fastest spreading mosquito-borne disease in the world, and a substantial public health problem due to its geographic spread, intensity, and resulting economic impact. The disease is endemic in Singapore, and has caused multiple outbreaks of unprecedented magnitude in the last two decades. Without a specific antiviral agent or a safe, effective and affordable vaccine for the disease, vector control remains the most effective way to control dengue transmission. The objective of this thesis is to develop spatially resolved accurate short-to-medium term dengue forecast systems, informed by mechanistic understanding of dengue transmission from previous field studies. Such systems could improve our understanding of factors that influence the transmission of dengue fever in Singapore, and potentially be used by government agencies for the planning of targeted vector control measures. / Data on dengue persistence, housing types, rainfall and seasonality was used to predict weekly dengue incidence at a housing level (i.e. dengue incidence in the high-rise and low-rise subzone groups) and at a residential subzone level. For both spatial resolutions, a separate multiple linear regression submodel was constructed for each forecast horizon of 1 to 12 weeks. Our housing-level model was able to achieve good predictions up to 6 weeks in advance, with predictive R² greater than 0.5 and total explained variance greater than 60%, but our subzone-level model was not as successful. At a housing level, we found that rainfall, housing type and seasonality predictors became relatively more important at longer forecast horizons. We also found that increased rainfall months before implies lower dengue incidence, and that rainfall influences seasonal variability in dengue incidence to a large degree. / The low-rise subzone group was also associated with higher dengue incidence than the high-rise subzone group. These findings support hypotheses from previous field studies on the roles of rainfall and urban hydrology in shaping the spatiotemporal distribution of dengue in Singapore. These risk factors of dengue could be included in current operational forecast systems to improve their predictive performance. / by Gin Kaijing Ong. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

abstract: Investigation into research literature was conducted in order to understand the impacts of traditional concrete construction and explore recent advancements in 3D printing technologies and methodologies. The research project focuses on the relationship between computer modeling, testing, and verification to reduce concrete usage in flexural elements. The project features small-scale and large-scale printing applications modelled by finite element analysis software and printed for laboratory testing. The laboratory testing included mortar cylinder testing, digital image correlation (DIC), and four pointbending tests. Results demonstrated comparable performance between casted, printed solid, and printed optimized flexural elements. Results additionally mimicked finite element models regarding failure regions. / Dissertation/Thesis / Masters Thesis Engineering 2020

Computational imaging di↵ers from traditional imaging system by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D- 3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color (a “rainbow”). For reconstruction, we derive an image formation model for recovering stationary 3D particle positions. 3D velocity estimation is achieved with a variant of 3D optical flow that accounts for both physical constraints as well as the rainbow image formation model. This velocity field can be used to refine the position estimate by adding physical priors that tie together all the time steps, forming a joint reconstruction scheme. In the second approach, we study the problem of reconstructing the 3D shape of underwater environments. The distortions from the moving water surface provide a changing parallax for each point on the underwater surface. We utilize this observation by jointly estimating both the underwater geometry and the dynamic shape of the water surface. To this end, we propose a novel di↵erentiable framework to tie together all parameters in an integrated image formation model. To our knowledge, this is the first solution that is capable to simultaneously retrieve the structure of dynamic water surfaces and static underwater scene geometry in the wild.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, February, 2020 / Manuscript. / Includes bibliographical references (pages 75-78). / Traditional Finite Element (FE) analysis requires the discretisation of continuous bodies into connected meshes of triangles and quadrilaterals (in 2D; tetrahedrals [tet] and hexahedrals [hex] in 3D) elements. Besides the restrictions due to compatibility of adjacent elements, one primary concern regarding mesh generation is that of minimizing the distortion of elements and the number of distorted elements so as to reduce the discretisation error. This has generally steered research in 2D mesh generation techniques away from grid-based methods, which tends to generate significant numbers of distorted elements; additionally, such methods are generally not considered at all in 3D mesh generation. Furthermore, significant amounts of man-hours are used during the meshing phase of FE analyses to partition and prescribe element types, where the ability to mesh portions of the geometry with hex elements is preferred over using tet elements in the mesh. The recent advances in the theory of Overlapping Finite Elements (OFE) now allow for the use of distorted elements without compromising on the accuracy of the FE analysis. However, a trade-off arises because more degrees of freedom (DOFs) are required at triangular (and tetrahedral) nodes. We propose the reintroduction of optimised 2D grid-based mesh generation techniques to decrease the DOFs in a way that is generalizable to arbitrary 3D geometries, as part of a step towards a truly automated meshing paradigm, referred to as the Automatic Meshing with Overlapping and Regular Elements (AMORE), which requires minimal-to-no input from the engineer. / by Angus Foo. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, September, 2019 / Manuscript. / Includes bibliographical references (pages 93-94). / This thesis follows and extends the discussion of Özer et al. (2011) on trust in forecast information sharing. We propose a method for belief learning and for updating. The effects of production cost (which indicate the risk) and market uncertainty (which indicates the accuracy of the private information) are analyzed quantitatively. Since complicated Nash equilibria from traditional game theory analysis often fail in real-life scenarios, we formulate simpler assumptions so that the strategies of both sides are not complicated. We compare the similarities and differences between the structure of our model and the structure of other behavioral models related to bounded rationality or cheap talk. We characterize how the supply chain environment changes trust and decisions. We find out that initial beliefs do not matter because they will be quickly adjusted by the market: the limiting behavior, as t --> [infinity], depends only on the retailers' trustworthiness and supply chain environment. Since the retailer's trustworthiness and belief is un-observable, we perform latent profile analysis to fit the model on the experiment conducted by Özer et al. (2011), and test the end game effect and out-of-sample fit. / by Pengbo Zhang. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Wavefront sensing is an old yet fundamental problem in adaptive optics. Traditional wavefront sensors are limited to time-consuming measurements, complicated and expensive setup, or low theoretically achievable resolution. In this thesis, we introduce an optically encoded and computationally decodable novel approach to the wavefront sensing problem: the Coded Shack-Hartmann. Our proposed Coded Shack-Hartmann wavefront sensor is inexpensive, easy to fabricate and calibrate, highly sensitive, accurate, and with high resolution. Most importantly, using simple optical flow tracking combined with phase smoothness prior, with the help of modern optimization technique, the computational part is split, efficient, and parallelized, hence real time performance has been achieved on Graphics Processing Unit (GPU), with high accuracy as well. This is validated by experimental results. We also show how optical flow intensity consistency term can be derived, using rigor scalar diffraction theory with proper approximation. This is the true physical law behind our model. Based on this insight, Coded Shack-Hartmann can be interpreted as an illumination post-modulated wavefront sensor. This offers a new theoretical approach for wavefront sensor design.

Dynamic model creation and support for the Lua scripting language was added to the Horizon Simulation Framework used for the verification and validation of system-level requirements. The addition of scripting support allows for subsystem models and their algorithms to be defined using a simpler scripting language, rather than C++. The Luabind library by Rasterbind software is used to expose the C++ algorithms and classes to Lua that are already defined in the framework. Interoperability between Lua and C++ code allows for the ease of use of a scripting language while utilizing the utility classes and functions already created. The dynamic model creation algorithm developed allows for the structure and logic of the simulated system to be defined completely using XML and text files as input to the framework. Dynamic model creation prevents the need to re-compile the framework every time small changes are made and greatly simplifies the changes required to simulate a different model. A runtime analysis shows that using a scripting language does not decrease the performance significantly, while increasing usability and decreasing the time required to set up simulations. Tests where the scripted code uses only classes and function exposed by luabind show that there is virtually no performance decrease when compared to the same algorithms in C++. Performance decreases as more of the algorithm implemented is executed in Lua. An example test scenario that represents the typical use of the framework showed only a minor decrease in performance. The multi-threaded scheduler developed works with the scripting support and allows for modern multi-core or multi-processor computers to be used most efficiently for simulations. This thesis describes the algorithms of dynamic model creation and scripting support, as well as the methodology used to expose C++ code to Lua. This thesis also presents the architecture changes required to support dynamic model creation, scripting support, and multi-threaded scheduling. Finally, runtime results of the added scripting support are presented.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 83-85). / The food safety problem has been challenging the traditional operating model of Chinese agricultural supply chain. In the recent decades, more and more agribusinesses and cooperatives in China have adopted contract farming to strengthen food safety control during the sourcing process. Meanwhile, several pricing schemes are applied to incentivize the quality-improving effort from producers of different risk attitudes and defaulting likelihoods. In this paper, we consider a producer-agribusiness supply chain with stochastic wholesale market price and random production yield. We model the three commonly-observed pricing schemes of contract farming in China: 1. Markup contract, 2. Fixed-price contract and 3. Protective-price contract. We characterize the equilibrium of the contracting game under each pricing scheme with risk-neutral and/or risk-averse producer. Furthermore, we investigate the optimal contract selections under different producer characteristics. We find that compared to the most frequently-used markup contract, the fixed-price and protective-price contract better incentivize the risk-averse producer to exert higher levels of quality-improving effort; In addition, switching from a markup contract to a protective-price contract or a fixed-price contract (under a certain threshold of defaulting rate) will achieve a win-win outcome where both the expected profit of the company and the utility of the risk-averse producer increase. Finally, we offer insights on the selection between the protective-price contract and fixed-price contract under different market price and production yield conditions. / by Yilin Yang. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 93-94). / In this thesis we present a reduced-dimension model for the density-driven hydrodynamic Rayleigh-Taylor instability. We motivate the project with experimental findings of a little-understood stabilizing effect of geometry and deviations of measured instability wavelength from theoretical predictions. We present novel methods of data analysis for the experimental data. We then present a reduced-dimension model for the governing equations of the system, Stoke's equations and Fick's law, using polynomial trial functions. We discuss the results and conduct a linear stability analysis of the reduced system. We compare the model to a finite element simulation of the full governing equations using COMSOL, and propose an optimization framework for the basis functions of the reduced model. The reduced model helps in developing physical intuition for the behavior of the instability in this confined geometry, and understanding the effects of certain parameters that are difficult to study experimentally or by simulating the full equations. / by Samar Alqatari. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program