Water flows through every aspect of life, yet the story of its delivery is only as reliable as the data that records it. In global benchmarking, such data is often uneven, incomplete, and rarely subjected to systematic validation, allowing anomalies to shape perceptions of performance before they are critically examined. This thesis addresses that gap by developing and evaluating a multi‐stage, data‐driven anomaly detection framework within the World Bank’s New International Benchmarking Network for Water and Sanitation Utilities (NewIBNET), situated at the intersection of data science, water governance, and digital ethics.

The framework weaves together four complementary layers – structural validation, rule‐based logical checks, peer comparison, and weighted prioritisation – transforming anomaly detection from a surface‐level cleaning task into a structured process of active quality assurance. Developed through an iterative, expert‐informed process, it is reproducible and adaptable, balancing statistical rigour with the contextual realities of the water sector so that each flag raised carries both analytical credibility and practical relevance.

Applied to the 2022–2024 NewIBNET dataset, the framework is assessed through robustness checks, a national case study of Indonesian utilities, and an expert survey. Results show that it improves anomaly interpretability, limits the propagation of flawed data into comparative analyses, and reduces review time from 75 hours to under 2 minutes – earning unanimous expert endorsement for operational deployment.

By translating the principles of automated, ethically grounded validation into a scalable methodology, this work advances the state of practice in anomaly detection for data‐scarce sectors. In shifting from red flags to real solutions, it demonstrates how automated validation can turn detection into action, building trust where data meets water, and enabling more transparent, equitable decisions in global water governance.

The Helmholtz equation is a famous equation in the world of physics with which the behavior of waves can be described. The equation is frequently used when studying, for example, seismic waves from earthquakes or electromagnetic waves of an MRI. It is also infamous for the difficulties that arise when trying to solve this equation with a computer. A great deal of research has been conducted over the last decades in order to find and improve numerical solution methods. As of today, no method is known that is feasible for general Helmholtz problems. This thesis investigates the root cause of problems when trying to solve the Helmholtz equation with domain decomposition methods. To this end, we work with the most basic form of the Helmholtz equation, which we will first construct. We then perform mathematical analysis to gain some insights into the root cause of problems. Numerical experiments are also conducted in order to investigate the behavior of the
mathematical models.

The numerical solution of the Helmholtz equation presents significant challenges in computational mathematics and scientific computing, particularly for high-frequency problems in heterogeneous media. This dissertation addresses these challenges through the development of high-performance iterative methods, focusing on the critical balance between numerical efficiency and practical implementation on modern computing architectures.

The research is motivated by the growing computational demands in seismic imaging and other wave propagation applications, where increasing frequencies and larger domains necessitate more efficient solution strategies. Traditional approaches often struggle with the combined challenges of wavenumber-dependent convergence, pollution errors, and substantial memory requirements, particularly for three-dimensional problems in heterogeneous media.

This work presents a comprehensive framework for solving large-scale Helmholtz problems through matrix-free parallel implementations of preconditioned iterative methods. The framework combines Complex Shifted Laplace Preconditioner (CSLP) with advanced deflation techniques, implemented in a manner that eliminates the need for explicit matrix storage while maintaining computational efficiency. A key innovation is the development of matrix-free implementations for higher-order deflation methods combined with the CSLP preconditioner, achieved through carefully designed re-discretization schemes that preserve the advantages of Galerkin coarsening.

The methodology progresses from two-dimensional implementations to fully three-dimensional frameworks, incorporating increasingly sophisticated preconditioning techniques. A significant achievement is the development of a matrix-free parallel multilevel deflation preconditioner that exhibits near wavenumber-independent convergence while maintaining excellent parallel scalability. The implementation utilizes a hybrid MPI+OpenMP parallelization strategy, effectively addressing both computational and memory challenges in extreme-scale scenarios.

Extensive numerical experiments validate the effectiveness of these methods across a range of problem types, from academic test cases to industrial-scale applications. Notably, the framework successfully resolves a challenging seismic model, involving approximately 3.8 billion degrees of freedom, while achieving 86\% parallel efficiency when scaling to 2304 CPU cores. This demonstration of practical viability for large-scale heterogeneous problems represents a significant advance in computational capabilities for seismic imaging applications.

The research makes several fundamental contributions to the field of numerical analysis and scientific computing. First, it establishes new approaches for matrix-free implementation of state-of-the-art preconditioners, significantly reducing memory requirements while maintaining numerical efficiency. Second, it demonstrates the achievement of close-to wavenumber-independent convergence through carefully designed deflation strategies in a parallel computing environment. Third, it provides a comprehensive framework for solving extreme-scale Helmholtz problems that combines numerical robustness with practical applicability.

The methodologies developed in this work contribute to the broader field of scientific computing, demonstrating how careful algorithm design, combined with modern computing architectures, can address previously intractable problems in wave propagation modeling.

This master's thesis provides information on the subject of option pricing theory. Moreover, this topic is linked with sustainable Finance, which is essential in the battle against climate change. Green financing is a growing phenomenon, and a green bond is only a relatively new example of a green financial derivative. A three-dimensional PDE is at hand to determine the price of a green bond (also called a coupon value), but this PDE can only be solved numerically. This master thesis aims to determine the stability of certain numerical schemes that can be used to find a solution to the PDE. We will use both forward and backward differences to derive the numerical schemes. After the derivation process, a Von Neumann analysis is performed to conclude whether or not the schemes are stable. The research reveals that most of the numerical schemes are not stable. Amongst others, this is caused by the positive value of the interest rate $r$, the value of the carbon price, and the absence of damping factors. In some numerical schemes, the amplification factor is larger than one, but not by much. In other schemes, we can reduce the amplification factor to increase the stability. This means that the numerical schemes can still be used to find a reasonable value for a green bond.

One of the most well-established codes for modeling non-linear Magnetohydrodynamics (MHD) for tokamak reactors is JOREK, which solves these equations with a Bézier surface based finite element method. This code produces a highly sparse but also very large linear system. The main solver behind the code uses the Generalized Minimum Residual Method (GMRES) with a physics-based preconditioner. Even with the preconditioner there are issues with memory and computation costs and the solver doesn’t always converge well. This work contains the first thorough study of the mathematical properties of the underlying linear system, enabling us to diagnose and pinpoint the cause of hampered convergence. In particular, analyzing the spectral properties of the matrix and the preconditioned system with numerical linear algebra techniques will open the door to research and investigate more performant solver strategies, such as projection methods.

Wave phenomena play an important role in many different applications such as MRI scans, seismology and acoustics [41, 49, 47]. At the core of such applications lies the Helmholtz equation, which represents the time-independent version of the wave equation. Simulating a Helmholtz problem numerically with accurate numerical solutions for large wave numbers is challenging. Numerical solvers for the Helmholtz problem have to balance having accurate numerical solutions, requiring a number of iterations to reach convergence that is independent of the wave number and solving with linear time complexity with respect to the grid nodes. Currently, there is no numerical Helmholtz solver that can satisfy these requirements at once.
We developed Schwarz domain decomposition preconditioners which leads to wave number independent convergence for wave numbers in 2D and 3D, while remaining to have accurate numerical solutions. The preconditioners use two-level Schwarz preconditioners, with the coarse problem being constructed using higher-order interpolation with quadratic rational Bézier curves. The developed domain decomposition preconditioners are designed to leverage parallel computing in the future in an attempt for the preconditioners to acquire the ability to solve with linear time complexity.

In this research, the preconditioner resulting in wave number independent convergence and the lowest iteration count is the two-level scaled hybrid Schwarz preconditioner with a coarse problem constructed using higher-order Bézier interpolation. This preconditioner uses a deflation method to remove unwanted eigenvalues. Removing these unwanted eigenvalues results in a clustering of the eigenvalues which is more favourable for GMRES. Currently, all the developed preconditioners suffer from high computational cost for large wave numbers, due to the coarse problem becoming large. Decreasing the coarse problem size of the preconditioners, while remaining to have wave number independent convergence, has shown to been unsuccessful. To better understand the required conditions for wave number independent convergence of the preconditioners, we investigated the relationship between the number of coarse grid nodes and the wave length, too see if there is anything generalizable about this relationship and wave number independent convergence of the solvers.
In conclusion, the balancing for a Helmholtz solver to have accurate numerical solutions, requiring a number of iterations to reach convergence that is independent of the wave number and solving with linear time complexity
is again shown to be difficult. This work provides the initial development and testing of promising wave number independent Helmholtz solvers, from which more research should follow that tackle its biggest computational problems.