<p>Let S be a polynomial ring K[x1,...,xn] over a field K and let F be a non-negatively graded free module over S generated by m basis elements. In this thesis, we study four kinds of upper bounds: degree bounds for Gröbner bases of submodules of F, bounds for arithmetic degrees of S-ideals, regularity bounds for radicals of S-ideals, and Stillman bounds. </p> <p><br></p> <p>Let M be a submodule of F generated by elements with degrees bounded above by D and dim(F/M)=r. We prove that if M is graded, the degree of the reduced Gröbner basis of M for any term order is bounded above by 2[1/2((Dm)^{n-r}m+D)]^{2^{r-1}}. If M is not graded, the bound is 2[1/2((Dm)^{(n-r)^2}m+D)]^{2^{r}}. This is a generalization of bounds for ideals in a polynomial ring due to Dubé (1990) and Mayr-Ritscher (2013).</p> <p><br></p> <p>Our next results are concerned with a homogeneous ideal I in S generated by forms of degree at most d with dim(S/I)=r. In Chapter 4, we show how to derive from a result of Hoa (2008) an upper bound for the regularity of sqrt{I}, which denotes the radical of I. More specifically we show that reg(sqrt{I})<= d^{(n-1)2^{r-1}}. In Chapter 5, we show that the i-th arithmetic degree of I is bounded above by 2*d^{2^{n-i-1}}. This is done by proving upper bounds for arithmetic degrees of strongly stable ideals and ideals of Borel type.</p> <p><br></p> <p>In the last chapter, we explain our progress in attempting to make Stillman bounds explicit. Ananyan and Hochster (2020) were the first to show the existence of Stillman bounds. Together with G. Caviglia, we observe that a possible way of making their results explicit is to find an effective bound for an invariant called D(k,d) and supplement it into their proof. Although we are able to obtain this bound D(k,d) and realize Stillman bounds via an algorithm, it turns out that the computational complexity of Ananyan and Hochster's inductive proof would make the bounds too large to be meaningful. We explain the bad behavior of these Stillman bounds by giving estimates up to degree 3.</p>

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In engineering, materials are often assumed to be homogeneous and isotropic; in actuality, material properties do change with sample direction and location. This variation is due to the anisotropy of the individual grains and their spatial distribution in the material. Currently there is a lack of communication between the design engineer, material scientist, and processor for solving multi-objective/constrained designs. If communication existed between these groups then materials could be designed for applications, instead of the reverse. Microstructure sensitive design introduces a common language, a spectral representation, where both design properties and microstructures are expressed. Using Hill bounds, effective elastic properties are expressed within the spectral representation. For the elastic properties, two FCC materials, copper and nickel, were chosen for computation and to demonstrate how symmetry enters into the methodology. This spectral representation renders properties as hyper-surfaces that translate through a multi-dimensional Fourier space depending on the property value of the hyper-surface. Property closures are generated by condensing the information contained within the multi-dimensional Fourier space into a 2-D representation. This compaction of information is beneficial for a quick determination of property limits for a particular alloy system. The design engineer can now dictate the critical design properties and receive sets of microstructures that satisfy the design objectives.

Ray tracing is a well-know technique for producing realistic graphics. However, the time necessary to generate images is unacceptably long. When producing the many frames that are necessary for animations, the time is magnified. Many methods have been proposed to reduce the calculations necessary in ray tracing. Much of the effort has attempted to reduce the number of rays cast or to reduce the number of intersection calculations. Both of these techniques exploit spatial coherence. These acceleration techniques are expanded not only to exploit spatial coherence but also to exploit temporal coherence in order to reduce calculations by treating animation information as a whole as opposed to isolating calculations to each individual frame. Techniques for exploiting temporal coherence are explored along with associated temporal bounding methods. By first ray tracing a temporally expanded scene, we are able to avoid traversal calculations in associated frames where object intersection is limited. This reduces the rendering times of the associated frames.

Thermalization is ubiquitous to all physical systems and is an essential assumption for the postulates of statistical mechanics. Generally, every system evolves under its own dynamics and reaches thermal equilibrium. In the quantum realm, thermal equilibrium is described by the Eigenstate Thermalization Hypothesis (ETH); hence every system that thermalizes is expected to follow ETH. Moreover, the thermalization process is always manifested as transport of matter and quantum information across the system. Thermalizing quantum systems with local interactions are expected to show diffusive transport of global conserved quantities and ballistic information spreading. The vast majority of many-body systems show the typical behavior described above. In this thesis, we study two mechanisms that break the standard picture of quantum thermalization. On the one hand, information spreading may be faster in the presence of long-range interactions. By simulating the Lieb-Robinson bounds in a spin chain with power-law decaying interactions, we distinguish the regime where the long-range character of the interactions becomes irrelevant for information spreading. On the other hand, the interplay of disorder and interactions can slow down transport, entering a sub-diffusive regime. We study this dynamical regime in an Anderson model on random regular graphs, where the emergence of a sub-diffusive regime before the localization transition is highly debated. Looking at long-range spectral correlations, we found that the sub-diffusive regime may be extended over the whole thermal phase of the model. Moreover, when disorder is strong enough, quantum many-body systems can undergo an ergodicity breaking transition to a many-body localized (MBL) phase. These systems do not follow ETH, so they present a challenge for conventional statistical mechanics. In particular, we study how the structure of local operator eigenstate matrix elements (central assumption of ETH) change between the thermal and MBL phase. A complete characterization of matrix elements of correlation functions is achieved via strong disorder quasi-degenerate perturbation theory. Furthermore, we study the MBL transition mechanism, which is still an open question due to the limitations of the available techniques for addressing that regime. Focusing on the avalanche mechanism, we simulate MBL spin chains coupled to a finite and infinite thermal bath. We could estimate the thermalization rate, which behaves as an order parameter and provide bounds for the actual critical disorder in the thermodynamic limit. We propose the existence of an intermediate MBL ``regime' where the system is slowly de-localizing, but relevant time scales are out-of-reach for current experiments and numerical simulations.

Heteroscedastic metamodeling has gained popularity as an effective tool for analyzing and optimizing complex stochastic systems. A heteroscedastic metamodel provides an accurate approximation of the input-output relationship implied by a stochastic simulation experiment whose output is subject to input-dependent noise variance. Several challenges remain unsolved in this field. First, in-depth investigations into the consistency of heteroscedastic metamodeling techniques, particularly from the sequential prediction perspective, are lacking. Second, sequential heteroscedastic metamodel-based level-set estimation (LSE) methods are scarce. Third, the increasingly high computational cost required by heteroscedastic Gaussian process-based LSE methods in the sequential sampling setting is a concern. Additionally, when constructing a valid uniform bound for a heteroscedastic metamodel, the impact of noise variance estimation is not adequately addressed. This dissertation aims to tackle these challenges and provide promising solutions. First, we investigate the information consistency of a widely used heteroscedastic metamodeling technique, stochastic kriging (SK). Second, we propose SK-based LSE methods leveraging novel uniform bounds for input-point classification. Moreover, we incorporate the Nystrom approximation and a principled budget allocation scheme to improve the computational efficiency of SK-based LSE methods. Lastly, we investigate empirical uniform bounds that take into account the impact of noise variance estimation, ensuring an adequate coverage capability. / Doctor of Philosophy / In real-world engineering problems, understanding and optimizing complex systems can be challenging and prohibitively expensive. Computer simulation is a valuable tool for analyzing and predicting system behaviors, allowing engineers to explore different scenarios without relying on costly physical prototypes. However, the increasing complexity of simulation models leads to a higher computational burden. Metamodeling techniques have emerged to address this issue by accurately approximating the system performance response surface based on limited simulation experiment data to enable real-time decision-making. Heteroscedastic metamodeling goes further by considering varying noise levels inherent in simulation outputs, resulting in more robust and accurate predictions. Among various techniques, stochastic kriging (SK) stands out by striking a good balance between computational efficiency and statistical accuracy. Despite extensive research on SK, challenges persist in its application and methodology. These include little understanding of SK's consistency properties, an absence of sequential SK-based algorithms for level-set estimation (LSE) under heteroscedasticity, and the increasingly low computational efficiency of SK-based LSE methods in implementation. Furthermore, a precise construction of uniform bounds for the SK predictor is also missing. This dissertation aims at addressing these aforementioned challenges. First, the information consistency of SK from a prediction perspective is investigated. Then, sequential SK-based procedures for LSE in stochastic simulation, incorporating novel uniform bounds for accurate input-point classification, are proposed. Furthermore, a popular approximation technique is incorporated to enhance the computational efficiency of the SK-based LSE methods. Lastly, empirical uniform bounds are investigated considering the impact of noise variance estimation.

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A series of anthropogenic disturbance conditions have altered the vegetation of the southern Appalachians during the past 200-years. The objective of this research was to identify the nature and timing of these vegetation changes in order to better understand the underlying causes. A total of 304 land surveys were collected for a small agricultural watershed from early settlement in 1787 through to the present day. Witness corners recorded tree species, shrubs, stumps, snags and non vegetative markers. Types of witness corners were tallied and tested for shifts in frequency across time periods. Tree species were also classified by silvical characteristics including sprouting capability, shade tolerance, and seed type and these groupings were tested for shifts in frequency across time periods. Landform bias of the witness corners was tested using references contained in the surveys. Results showed significant shifts in white oak (Quercus alba L.), chestnut (Castanea dentate Marsh. Borkh.), chestnut oak (Quercus prinus Wild.), black oak (Quercus velutina Lam.), red oak(Quercus rubra L.), black locust (Robinia pseudoacacia L.), yellow poplar (Liriodendron tulipifera L.), and scarlet oak (Quercus coccinea Muenchh.). The central change was a steady decline in white oak, probably due to the absence of fire and changes in soil properties. Chestnut replaced white oak as the dominant species, but was removed by chestnut blight in the 1930's. Sprouting capability appeared to be the most important silvical characteristic across all species. / Master of Science

Metamodeling is regarded as a powerful analysis tool to learn the input-output relationship of a system based on a limited amount of data collected when experiments with real systems are costly or impractical. As a popular metamodeling method, Gaussian process regression (GPR), has been successfully applied to analyses of various engineering systems. However, GPR-based metamodeling for time-dependent systems (TDSs) is especially challenging due to three reasons. First, TDSs require an appropriate account for temporal effects, however, standard GPR cannot address temporal effects easily and satisfactorily. Second, TDSs typically require analytics tools with a sufficiently high computational efficiency to support online decision making, but standard GPR may not be adequate for real-time implementation. Lastly, reliable uncertainty quantification is a key to success for operational planning of TDSs in real world, however, research on how to construct adequate error bounds for GPR-based metamodeling is sparse. Inspired by the challenges encountered in GPR-based analyses of two representative stochastic TDSs, i.e., load forecasting in a power system and trajectory prediction for unmanned aerial vehicles (UAVs), this dissertation aims to develop novel modeling, sampling, and statistical analysis techniques for enhancing the computational and statistical efficiencies of GPR-based metamodeling to meet the requirements of practical implementations. Furthermore, an in-depth investigation on building uniform error bounds for stochastic kriging is conducted, which sets up a foundation for developing robust GPR-based metamodeling techniques for analyses of TDSs under the impact of strong heteroscedasticity. / Ph.D. / Metamodeling has been regarded as a powerful analysis tool to learn the input-output relationship of an engineering system with a limited amount of experimental data available. As a popular metamodeling method, Gaussian process regression (GPR) has been widely applied to analyses of various engineering systems whose input-output relationships do not depend on time. However, GPR-based metamodeling for time-dependent systems (TDSs), whose input-output relationships depend on time, is especially challenging due to three reasons. First, standard GPR cannot properly address temporal effects for TDSs. Second, standard GPR is typically not computationally efficient enough for real-time implementations in TDSs. Lastly, research on how to adequately quantify the uncertainty associated with the performance of GPR-based metamodeling is sparse. To fill this knowledge gap, this dissertation aims to develop novel modeling, sampling, and statistical analysis techniques for enhancing standard GPR to meet the requirements of practical implementations for TDSs. Effective solutions are provided to address the challenges encountered in GPR-based analyses of two representative stochastic TDSs, i.e., load forecasting in a power system and trajectory prediction for unmanned aerial vehicles (UAVs). Furthermore, an in-depth investigation on quantifying the uncertainty associated with the performance of stochastic kriging (a variant of standard GPR) is conducted, which sets up a foundation for developing robust GPR-based metamodeling techniques for analyses of more complex TDSs.