Publications Server of the Weierstrass Institute for Applied Analysis and Stochastics
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On non-autonomous parabolic equations with measure-valued right hand sides and applications to optimal control
Full text linkThe main aim of this paper is to develop a theory for non-autonomous parabolic equations with time-dependent measures on the spatial domain appearing as right hand sides. Restricting these measures to ones which have their supports on 'curves' or 'surfaces' -- the latter understood in the sense of geometric measure theory -- we succeed in interpreting them as distributional objects from a (negatively indexed) Sobolev--Slobodetskii space with differentiability index close to minus one. For these indices a tailor suited parabolic theory is established, based on previous results. It is also demonstrated that the proposed frame work is well-suited for optimal control with controls acting on sub-manifolds
First passage percolation on random geometric graphs and an application to shortest-Path trees
Full text linkWe consider Euclidean first-passage percolation on a large family of connected random geometric graphs in the d-dimensional Euclidean space encompassing various well-known models from stochastic geometry. In particular, we establish a strong linear growth property for shortest-path lengths on random geometric graphs which are generated by point processes. We consider the event that the growth of shortest-path lengths between two (end-) points of the path does not admit a linear upper bound. Our linear growth property implies that the probability of this event tends to zero sub-exponentially fast if the direct (Euclidean) distance between the endpoints tends to infinity. Besides, for a wide class of stationary and isotropic random geometric graphs, our linear growth property implies a shape theorem for the Euclidean first-passage model defined by such random geometric graphs. Finally, this shape theorem can be used to investigate a problem which is considered in structural analysis of fixed-access telecommunication networks, where we determine the limiting distribution of the length of the longest branch in the shortest-path tree extracted from a typical segment system if the intensity of network stations converges to zero
Foundational Competencies and Specializations of a Research Software Engineer
No full textThe term Research Software Engineer (RSE) emerged to represent individuals working in the research community but focusing on software development. It has been widely adopted, and has several high-level definitions. However, their work varies depending on the institutional context. At one extreme, RSE roles look similar to traditional researchers. At the other extreme, they resemble an industrial software engineer. Most RSE roles inhabit the spectrum in between. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies they require is challenging. In this summarized community paper, we define the broad notion of RSEs, explore their different types of work, and define a list of competencies and values that frame their general identity. Further research and training can build upon and expand this foundation, and we expect graduates and practitioners to have a larger, more diverse set of skills than outlined here
Out-of-core Constrained Delaunay Tetrahedralizations for Large Scenes
No full textTetrahedralization algorithms are used for many applications such as Ray Tracing and Finite Element Methods. For most of the applications, constrained tetrahedralization algorithms are chosen because they can preserve input triangles. The constrained tetrahedralization algorithms developed so far might suffer from a lack of memory. We propose an out-of-core near Delaunay constrained tetrahedralization algorithm using the divide-and-conquer paradigm to decrease memory usage. If the expected memory usage is below the user-defined memory limit, we tetrahedralize using TetGen. Otherwise, we subdivide the set of input points into two halves and recursively apply the same idea to the two halves. When compared with the TetGen, our algorithm tetrahedralizes the point clouds using less amount of memory but takes more time and generates tetrahedralizations that do not satisfy the Delaunay criterion at the boundaries of the merged regions. We quantify the error using the aspect-ratio metric. The difference between the tetrahedralizations that our approach produce and the Delaunay tetrahedralization are small and the results are acceptable for most applications
A model of gravitational differentiation of compressible self-gravitating planets
No full textWe present a dynamic model for inhomogeneous viscoelastic media at finite strains. The model features a Kelvin--Voigt rheology, and includes a self-generated gravitational field in the actual evolving configuration. In particular, a fully Eulerian approach is adopted. We specialize the model to viscoelastic (barotropic) fluids and prove existence and a certain regularity of global weak solutions by a Faedo--Galerkin semi-discretization technique. Then, an extension to multi-component chemically reacting viscoelastic fluids based on a phenomenological approach by Eckart and Prigogine, is advanced and studied. The model is inspired by planetary geophysics. In particular, it describes gravitational differentiation of inhomogeneous planets and moons, possibly undergoing volumetric phase transitions
Optimal control of a reaction-diffusion epidemic model with non-compliance
No full textIn this paper, we consider an optimal distributed control problem for a reaction-diffusion-based SIR epidemic model with human behavioural effects. We develop a model wherein non-pharmaceutical intervention methods are implemented, but a portion of the population does not comply with them, and this non-compliance affects the spread of the disease. Drawing from social contagion theory, our model allows for the spread of non-compliance parallel to the spread of the disease. The quantities of interest for control are the reduction in infection rate among the compliant population, the rate of spread of non-compliance and the rate at which non-compliant individuals become compliant after, e.g., receiving more or better information about the underlying disease. We prove the existence of global-in-time solutions for fixed controls and study the regularity properties of the resulting control-to-state map. The existence of optimal control is then established in an abstract framework for a fairly general class of objective functions. Necessary first–order optimality conditions are obtained via a Lagrangian-based stationarity system. We conclude with a discussion regarding minimisation of the size of infected and non-compliant populations and present simulations with various parameters values to demonstrate the behaviour of the model
Refined stability estimates for mixed problems by exploiting semi norm arguments
Full text linkRefined stability estimates are derived for classical mixed problems. The novel emphasis is on the importance of semi norms on data functionals, inspired by recent progress on pressure-robust discretizations for the incompressible Navier--Stokes equations. In fact, kernels of these semi norms are shown to be connected to physical regimes in applications and are related to some well-known consistency errors in classical discretizations of mixed problems. Consequently, significantly sharper stability estimates for solutions close to these physical regimes are obtained
Large-scale stochastic simulation of open quantum systems
Full text linkUnderstanding the precise interaction mechanisms between quantum systems and their environment is crucial for advancing stable quantum technologies, designing reliable experimental frameworks, and building accurate models of real-world phenomena. However, simulating open quantum systems, which feature complex non-unitary dynamics, poses significant computational challenges that require innovative methods to overcome. In this work, we introduce the tensor jump method (TJM), a scalable, embarrassingly parallel algorithm for stochastically simulating large-scale open quantum systems, specifically Markovian dynamics captured by Lindbladians. This method is built on three core principles where, in particular, we extend the Monte Carlo wave function (MCWF) method to matrix product states, use a dynamic time-dependent variational principle (TDVP) to significantly reduce errors during time evolution, and introduce what we call a sampling MPS to drastically reduce the dependence on the simulation's time step size. We demonstrate that this method scales more effectively than previous methods and ensures convergence to the Lindbladian solution independent of system size, which we show both rigorously and numerically. Finally, we provide evidence of its utility by simulating Lindbladian dynamics of XXX Heisenberg models up to a thousand spins using a consumer-grade CPU. This work represents a significant step forward in the simulation of large-scale open quantum systems, with the potential to enable discoveries across various domains of quantum physics, particularly those where the environment plays a fundamental role, and to both dequantize and facilitate the development of more stable quantum hardware
NUSOD 2025
No full textThe NUSOD conference connects theory and practice in optoelectronics. Academic researchers, device engineers, and software developers are invited to discuss the advancement and the practical use of numerical methods in photonics and electronics