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High-Resolution Geophysical Images of Static Crustal Structure and Time-Dependent Glacier Flow
Get PDFThe emerging availability of high-quality geophysical observations motivates the development of new methodologies to better extract the key information contained in the datasets. In this thesis, I present methodological developments utilizing two types of geophysical data. Firstly, in Chapter 2, I make use of the observations recorded at the newly-available dense seismic arrays and propose a new method for estimating the seismic receiver functions (RFs). RFs have been widely used in global seismology to probe the structural discontinuities in the interior of the Earth. By exploiting the coherency in RFs at neighboring stations, the new method adds the RF coherency as a key constraint in RF estimation, which directly addresses issues such as non-uniqueness and over-fitting in conventional ways for RF estimations. I show a pilot application of this method to real data that demonstrates its advantages on obtaining high-quality RFs on short-term (e.g., one month) high-density seismic profiles. Secondly, in Chapter 3 and Chapter 4, I take advantage of the temporally dense Synthetic-Aperture-Radar (SAR) imagery. In these two main chapters of my thesis, I focus on understanding the temporal variations in the buttress stress of Antarctic ice shelves and develop new methodologies for observing tidally-induced ephemeral grounding of ice shelves on the sub-shelf bathymetric highs. This observational study provides new insights into the buttressing effect of ice shelves and improves our understanding of the dynamics of Antarctic ice flow including the short-term (days to weeks) response to tidal forcing and the long-term (tens to hundreds of years) response to changes in climate. Specifically, in Chapter 3, I illustrate the methodological development and an application to Rutford Ice Stream (RIS), West Antarctica; in Chapter 4, I further apply the new methods to Evans Ice Stream (EIS), an ice-stream-shelf system significantly larger than the RIS with multiple upstream tributaries and complex grounding line. At both RIS and EIS, I find abundant zones of ephemeral grounding in the vicinity of the grounding zone. These two studies provide direct evidence for the asymmetric response of ice flows to tidal forcing, which causes the observed strong fortnightly variation in horizontal flow. With the projected oceanic warming, our observations of ephemeral grounding will help quantify the increase in ice flow rate in the long-term caused by the loss of buttressing stress due to ice-shelf thinning.</p
Representation of the Semantic Structures: from Discovery to Applications
Get PDFThe world surrounding us is full of structured entities. Scenes can be structured as the sum of objects arranged in space, objects can be decomposed into parts, and even small molecules are composed of atoms. As humans can organize and structure many concepts into smaller components, structural representation has become a powerful tool for various applications. Computer vision utilizes the part-based representation for classical object detection and categorization tasks, and computational neuroscientists use the structural representation to achieve an interpretable and low-dimensional encoding for behavior analysis. Furthermore, structural encoding of the molecules allows the application of machine learning models to optimize experimental reaction conditions in organic chemistry.
To perform the high-level tasks described above, accurate detection of the structural component should be accomplished in advance. In this dissertation, we first propose methods to improve the pose estimation algorithm, where the task is to localize the semantic parts of the target instance from a 2D image. As the collection of a large number of human annotations is a prerequisite for the task to be successful, we aim to design a model that automatically discovers the structure information from the visual inputs without supervision. Lastly, we demonstrate the efficacy of the structural representation by applying it to various scientific applications such as behavior analysis and organic chemistry.</p
Photonic and Phononic Band Gap Engineering for Circuit Quantum Electrodynamics and Quantum Transduction
Get PDFThe ability to pattern materials at the wavelength and sub-wavelength scale has led to the concept of photonic crystals and metamaterials - artificially engineered structures that exhibit electromagnetic properties not found in conventional materials. Such engineered structures offer the ability to slow down and even inhibit the propagation of electromagnetic waves giving rise to a photonic band gap and a sharply varying photonic density of states.
Quantum emitters in the presence of an electromagnetic reservoir with varying density of states can undergo a rich set of dynamical behavior. In particular, the reservoir can be tailored to have a memory of past interactions with emitters, in contrast to memory-less Markovian dynamics of typical open systems. In part 1 of this thesis, we investigate the non-Markovian dynamics of a superconducting qubit strongly coupled to a superconducting metamaterial waveguide engineered to have both a sharp spectral variation in its transmission properties and a slowing of light by a factor of 650. Tuning the qubit into the spectral vicinity of the passband of this slow-light waveguide reservoir, we observe a 400-fold change in the emission rate of the qubit, along with oscillatory energy relaxation of the qubit resulting from the beating of bound and radiative dressed qubit-photon states. Further, upon addition of a reflective boundary to one end of the waveguide, we observe revivals in the qubit population on a timescale 30 times longer than the inverse of the qubit’s emission rate, corresponding to the round-trip travel time of an emitted photon. With this superconducting circuit platform, future studies of multi-qubit interactions via highly structured reservoirs and the generation of multi-photon highly entangled states are possible.
While microwave frequency superconducting circuits are near ideal testbeds for quantum electrodynamics experiments of the type discussed in part 1, microwave photons are not well suited for transmission of quantum information over long distances due to the presence of a large thermal background at room temperature. Optical photons are ideal for quantum communication applications due to their low propagation loss at room temperature. Coherent transduction of single photons from the microwave to the optical domain has the potential to play a key role in quantum networking and distributed quantum computing. In part 2 of this thesis, we extend the notion of band gap engineering to the optical and acoustic domain and present the design of a piezo-optomechanical quantum transducer where transduction is mediated by a strongly hybridized acoustic mode of a lithium niobate piezoacoustic cavity attached to a silicon optomechanical crystal patterned on a silicon-on-insulator substrate. We estimate an intrinsic transduction efficiency of 29% with <0.5 added noise quanta when our transducer is resonantly coupled to a superconducting transmon qubit and operated in pulsed mode. Our design involves on-chip integration of a superconducting qubit with the piezo-optomechanical transducer. Absorption of stray photons from the optical pump used in the transduction process is known to cause excess decoherence and noise in the superconducting circuit. The recovery time of the superconducting circuit after the optical pulse sets a limit on the transducer repetition rate. We fabricate niobium based superconducting circuits on a silicon substrate and test their response to illumination by a 1550 nm laser. We find a recovery time of ~ 10 μs, indicating that a repetition rate of 10 kHz should be possible. Combined with the expected efficiency and noise metrics of our design, we expect that a transducer in this parameter regime would be suitable to realize probabilistic schemes for remote entanglement of superconducting quantum processors. We conclude by discussing some of the challenges associated with fabricating niobium superconducting qubits and lithium niobate piezoacoustic devices on silicon-on-insulator substrates and provide initial steps towards realizing our transducer design in the lab.</p
Development and Scaling of Differentiation Circuit Architectures for Improving the Evolutionary Stability of Burdensome Functions in E. coli
Get PDFWith advances in the sequencing and synthesis of DNA, automation, and computation, we are increasingly able to rapidly and reliably program functions into cells. However, because the functions we engineer cells to perform are often both unnecessary for the cell’s survival and burdensome to cell growth, mutation and natural selection can rapidly lead to loss of function. Though numerous strategies have made headway, improving the evolutionary stability of engineered functions remains a goal of the synthetic biology community. To address this problem generally, we developed a strategy relying on integrase-mediated recombination which allows non-producing progenitor cells to differentiate at a tunable rate, thereby continuously replenishing producer cells expressing the orthogonal T7 RNAP. While this strategy removes selective pressure for mutations inactivating the function of interest in the progenitor cell population, a strategy of terminal differentiation, in which the capacity of differentiated cells to grow is limited, was necessary to prevent the expansion of such mutations in the differentiated cell population. To experimentally implement terminal differentiation, we co-opted the R6K plasmid system, using differentiation to simultaneously activate expression of T7 RNAP, and inactivate expression of π protein (an essential factor for R6K plasmid replication), thereby allowing limitation of differentiated cell growth through antibiotic selection. Critically, we demonstrated computationally that terminal differentiation endows the circuit with robustness to mutations which disrupt T7 RNAP driven expression, and to plasmid instability effects that result in decreased expression. Intuitively and computationally identifying the category of mutations which disrupt the differentiation process as the Achilles's heel of terminal differentiation, we developed a redundant architecture using a novel split-π protein system which required 2 mutations to break the circuit. We experimentally demonstrated a trade-off between rate of production and duration of function as the differentiation rate is tuned, an increased benefit of terminal differentiation with higher-burden expression, and that redundancy improves the evolutionary stability of the terminal differentiation architecture. Specifically we achieve a maximum of ~2.8x (single-cassette terminal differentiation) and ~4.2x (redundant terminal differentiation) the total fluorescent protein production achieved from comparable high-burden naive expression in which all cells inducibly express T7 RNAP. We further demonstrate differentiation can enable the expression of even toxic functions, and develop a terminal differentiation circuit architecture which will allow the degree of redundancy and therefore the evolutionary stability of the architecture to be scaled to arbitrary degrees.</p
Applications of Machine Learning to Finite Volume Methods
Get PDFThe finite volume method (FVM) has been one of the primary tools of computational fluid dynamics (CFD) for many decades. This method allows for the approximate solution of a partial differential equation (PDE) to be determined by breaking up a problem with no analytical solution into smaller pieces that can be solved together to get a physically realistic simulation. These algorithms can even be used for PDEs with discontinuous solutions, though they must be carefully designed for those situations because they cannot assume any level of smoothness in the solution. An FVM that has been designed for PDEs with discontinuous solutions is referred to as a shock-capturing method. For most of their history, FVM algorithms have been developed using rigorous mathematical arguments to formally maximize the order of convergence of the solution as the grid is refined. However, these arguments depend on the solution to the PDE being smooth, and therefore do not apply to shock-capturing methods. Instead, shock-capturing methods have traditionally been designed using human intuition to create algorithms that then perform well empirically. In this thesis, we instead follow a data-driven approach to train neural networks to use for enhanced FVM methods.
By including a neural network in our FVM, we can use empirical data to optimize the algorithm. We can also utilize ideas from traditional FVM algorithms to create hybrid methods that have tunable parameters and maintain convergence guarantees present in FVMs that have been designed by hand. We explore these hybrid methods in a variety of settings. First, we create a general-purpose shock-capturing method WENO-NN by hybridizing the popular shock-capturing method WENO-JS with a neural network. Additionally, we develop a network architecture, called FiniteNet, that can be used to learn a coarse-graining model associated with a specific PDE and embed it into an FVM scheme. Finally, we also explore the idea of using transfer learning to further improve the WENO-NN for specific problems and name the resulting algorithm WENO-TL. We demonstrate experimentally that this hybrid approach results in methods that can offer similar error levels as traditional FVMs at less computational cost. Although the neural network increases the computational cost of one evaluation of our hybrid FVM, these methods also allow the simulation to be carried out on a coarser grid, leading to a net reduction in both simulation time and memory usage.</p
Isomer Separation of Multifunctional Atmospheric Compounds Using Gas Chromatography and Chemical Ionization Mass Spectrometry
Get PDFOxygenated volatile organic compounds are a group of carbon-containing species that include one or more functional groups. They are formed during oxidation of hydrocarbons in the atmosphere. Afterwards, they readily undergo atmospheric processing, which—depending on their chemical properties—can lead to the formation of harmful pollutants, such as ozone or secondary organic aerosols (SOA). Prolonged exposure to either compound can negatively impact human health.
Unfortunately, most existing analytical techniques struggle to quantify the concentrations of the majority of OVOCs due to their characteristic low abundances and high reactivities. In addition, most of these compounds are also made up of a complex mixture of isomers that few instruments are able to resolve. Since even slight changes in structure can impact an OVOC’s atmospheric fate, this can lead to uncertainties when elucidating their chemical mechanisms. As a result, despite decades of research, there are still many outstanding questions pertaining to atmospheric processing of OVOCs and, by extension, their impact on air quality.
To combat this issue, novel instrumentation was developed that can provide accurate, isomer-resolved measurements of a wide variety of OVOCs, which it achieves by combining the sensitive, specific nature of gas chromatography (GC) with the equally sensitive, yet non-invasive aspects of chemical ionization mass spectrometry (CIMS). To showcase its capabilities, these new instrumental methods are applied to the study of isoprene oxidation. More specifically, we report new insights into the isomer-specific loss processes of isoprene-derived hydroxy nitrates. Inclusion of our findings into atmospheric models can greatly improve our simulations of NOₓ, ozone, and nitric acid.</p
Waveguide Quantum Electrodynamics in Superconducting Circuits
Get PDFAchieving an efficient interface of light and matter has been a principal goal in the field of quantum optics. A burgeoning paradigm in the study of light-matter interface is waveguide quantum electrodynamics (QED), where quantum emitters are coupled to a common one-dimensional waveguide channel. In this scenario, cooperative effects among quantum emitters emerge as a result of real and virtual exchange of photons, giving rise to new ways of controlling matter.
Superconducting quantum circuits offer an exciting platform to study quantum optics in the microwave domain with artificial quantum emitters interfaced to engineered photonic structures on chip. Beyond revisiting the experiments performed in atom-based platforms, superconducting circuits enable exploration of novel regimes in quantum optics that are otherwise prohibitively challenging to achieve. Moreover, the unprecedented level of control over individual quantum degrees of freedom and good scalability of the system provided by state-of-the-art circuit QED toolbox set a promising direction towards the study of quantum many-body phenomena.
In this thesis, I discuss waveguide QED experiments performed in superconducting quantum circuits where transmon qubits are coupled to engineered microwave waveguides. Employing the high flexibility and controllability of superconducting quantum circuits, we realize and explore various schemes for generating waveguide-mediated interactions between superconducting qubits. We also demonstrate an intermediate-scale quantum processor based on a dispersive waveguide QED system involving ten superconducting qubits, exploring quantum many-body dynamics in a highly controllable fashion. The work described in the thesis marks an important step towards the construction of scalable architectures for quantum simulation of many-body models and realization of efficient coupling schemes for quantum computation.</p
Accelerated Computational Micromechanics
Get PDFThe development of new materials is an important component of many cutting edge technologies such as space technology, electronics and medical devices. The properties of advanced materials involve phenomena across multiple scales. The material may be heterogeneous on a scale that is small compared to that of applications, or may spontaneously develop fine-scale structure. Numerical simulation of such phenomena can be an effective tool in understanding the complex physics underlying these materials, thereby assisting the development and refinement of such materials, but can also be challenging.
This thesis develops a new method to exploit the use of graphical processing units and other accelerators for the computational study of complex phenomena in heterogeneous materials. The governing equations are nonlinear partial differential equations, typically second order in space and first order in time. We propose an operator-splitting scheme to solve these equations by observing that these equations come about by a composition of linear differential constraints like kinematic compatibility and balance laws, and nonlinear but local constitutive equations. We formulate the governing equation as an incremental variational principle. We treat both the deformation and the deformation gradient as independent variables, but enforce kinematic compatibility between them as a constraint using an augmented Lagrangian. The resulting local-global problem is solved using the alternating direction method of multipliers. This enables efficient implementation on massively parallel graphical processing units and other accelerators. We use the study of elastic composites in finite elasticity to verify the method, and to demonstrate its numerical performance. We also compare the performance of the proposed method with that of other emerging approaches.
We apply the method to understand the mechanisms responsible for a remarkable in-plane liquid-like property of liquid crystal elastomers (LCEs). LCEs are rubber-like solids where rod-like nematic molecules are incorporated into the main or a side polymer chain. They undergo isotropic to nematic phase transition accompanied by spontaneous deformation which can be exploited for actuation. Further, they display a soft behavior at low temperatures due to the reorientation of the nematic directors. Recent experiments show that LCEs exhibit an in-plane liquid-like behavior under multiaxial loading, where there is shear strain with no shear stress. Our numerical studies of LCEs provides insights into the director distribution and reorientation in polydomain specimens, and how these lead to the observed liquid-like behavior. The results show good agreement with experimental observations. In addition to providing insight, this demonstrates the ability of our computational approach to study multiple coupled fields.
The core ideas behind the method developed in this thesis are then applied elsewhere. First, we use it to study multi-stable deployable engineering structures motivated by origami. The approach uses two descriptions of origami kinematics, angle/face based approach and vertex/truss based approach independently, and enforces the relationship between them as a constraint. This is analogous to the treatment of kinematic compatibility above where both the deformation and deformation gradient are used as independent variables. The constraint is treated using a penalty. Stable and rigid-foldable configurations are identified by minimizing the penalty using alternate directions, and pathways between stable states are found using the nudged elastic band method. The approach is demonstrated using various examples.
Second, we use a balance law or equilibrium to the problem of determining the stress field from high resolution x-ray diffraction. This experimental approach determines the stress field locally, and errors lead to non-equilibriated fields. It is hypothesized that imposing equilibrium leads to a more accurate stress reconstruction. We use Hodge decomposition to project a non-equilibriated stress field onto the divergence-free (equilibriated) subspace. This projection is numerically implemented using fast Fourier transforms. This method is first verified using synthetic data, and then applied to experimental data obtained from a beta-Ti alloy. It results in large corrections near grain boundaries.</p
Probing Water Below the Surface: Insights from Seismic Interferometry with Conventional and DAS Array
Get PDFWater is essential to our daily lives, yet its subsurface behavior remains challenging to track using remote observations. By extracting seismic waves traveling through the Earth, seismic interferometry is a powerful tool to image the Earth’s interior, in particular the long-term and short-term behavior of water circulation. With conventional dense seismic networks and emerging distributed acoustic sensing (DAS), I demonstrate how seismic interferometry brings new insights on water below the surface ranging from the depths of the mantle transition zone (MTZ) to the subsurface aquifers of our planet.
By applying a novel inter-source interferometry method that turns deep earthquakes into virtual seismometers, I not only present evidence for an intermediate-scale metastable olivine wedge and small-scale intra-slab scatterers in the MTZ beneath the Japan Sea, but also reveal their dimensions and velocity perturbations more accurately than before. Beyond the relative independent scales of slab structures, these results point toward a consistent picture of transformational faulting of metastable olivine as the initiation mechanism of deep earthquakes, petrologic processes associated with dehydration of subducting slabs, and an extremely dry slab core below 410-km. Borrowing the idea from inter-source interferometry, I develop a slab operator method by utilizing the waveform broadening due to the high-velocity anomaly. With synthetic tests and real data, I illustrate the feasibility of this method for accurately determining large-scale slab velocity perturbations.
Shifting from the Earth’s interior to the subsurface, I investigate the feasibility of vadose zone water monitoring with DAS. DAS provides an affordable and scalable solution for deploying ultra-dense seismic arrays by transforming existing optic-fiber cables into thousands of seismic sensors. With two years of ambient noise recorded on the Ridgecrest DAS array, the time-lapse images of seismic changes (dv/v) reveal an unprecedented high-resolution spatiotemporal evolution of water saturation in vadose zone. A striking correlation between the dv/v amplitude and the sedimentary thickness is observed, while the frequency analysis of dv/v measurements suggests an uppermost 10 m hydrologic source as the cause for dv/v temporal variability. The results demonstrate the great potential of DAS for long term subsurface water monitoring.</p
Mechanical Interactions Between Water and the Solid Earth: from Quasi-Static Geodetic Deformation to Dynamic Fault Slip
Get PDFMechanical interactions between Earth's solid interior and its hydrosphere are central to many geophysical problems of crucial societal importance: Changing conditions in the global water cycle deform the solid Earth; the groundwater storage capacity of aquifer systems is controlled by its interaction with geological materials; and crustal water - either natural occurring or added through anthropogenic activities - affects earthquakes and fault slip processes. In this thesis, we investigate some of these interactions by harnessing recent developments in the fields of satellite geodesy, statistical data analysis and elastodynamic earthquake modelling. We start by developing a procedure to identify and extract seasonal deformation signals associated with hydrological loading of the solid Earth from geodetic time series in Chapter 1. In Chapters 2 and 3, we consider the examples of the Ozarks Plateau (central United States) and Sacramento Valley (California) to establish a methodology for characterizing poroelastic deformation arising from groundwater variations with space-based geodesy. Then, in Chapter 4, we develop a model to simulate fault slip due to crustal water injections and calibrate it against a well-instrumented field experiment on a natural fault. We conclude by deriving a theoretical understanding of these fault slip simulations by considering the simple case of a fixed-length pressurized zone in Chapter 5. Overall, our work provides key insights for extracting and using different sources of hydrogeodetic signals as well as for modeling and understanding fluid-induced fault slip processes, which is becoming increasingly important in a world faced with water scarcity, a changing climate and an increased reliance on groundwater and geoenergy resources.</p