arXiv subjects

Michael Lange

Publications and source records attributed to Michael Lange.

At least 19 recordsLinked to original sources

arXiv2026

Some More Rigid Real Closed Fields

We extend a construction of Marker and Steinhorn for producing countable non-archimedean real closed fields which have no non-trivial automorphisms. The original construction produces such a field of transcendence degree two over the field of real algebraic numbers. We show how to use the same method to produce examples of all finite transcendence degrees and of countably infinite transcendence degree over the real algebraic numbers, answering a question of Marker and Steinhorn.

math.LO↗

arXiv2025

Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation

Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.

cs.RO↗

arXiv2022

Vibrational properties of LiNbO$_3$ and LiTaO$_3$ under uniaxial stress

Structural strain severely impacts material properties such as the linear and non-linear optical response. Moreover, strain plays a key role, e.g., in the physics of ferroelectrics and in particular of their domain walls. $μ$-Raman spectroscopy is a well-suited technique for the investigation of such strain effects, as it allows to measure the lattice dynamics locally. However, quantifying and reconstructing strain fields from Raman maps requires knowledge on the strain dependence of phonon frequencies. In this work, we have analyzed both theoretically and experimentally the phonon frequencies in the widely used ferroelectrics lithium niobate and lithium tantalate as a function of uniaxial strain via density functional theory and $μ$-Raman spectroscopy. Overall, we find a good agreement between our $ab$ $initio$ models and the experimental data performed with a stress cell. The majority of phonons show an increase in frequency under compressive strain, while the opposite is observed for tensile strains. Moreover, for E-type phonons, we observe the lifting of degeneracy already at moderate strain fields (i.e. at $\pm0.2~\%$) along the x and y directions. This work hence allows for the systematic analysis of 3D strains in modern-type bulk and thin-film devices assembled from lithium niobate and tantalate.

cond-mat.mtrl-sci↗

arXiv2020

Quantum Computer Systems for Scientific Discovery

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by co-designing full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2--10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21--22, 2019 in Alexandria, VA.

quant-ph↗

arXiv2020

Architecture and performance of Devito, a system for automated stencil computation

Stencil computations are a key part of many high-performance computing applications, such as image processing, convolutional neural networks, and finite-difference solvers for partial differential equations. Devito is a framework capable of generating highly-optimized code given symbolic equations expressed in Python, specialized in, but not limited to, affine (stencil) codes. The lowering process---from mathematical equations down to C++ code---is performed by the Devito compiler through a series of intermediate representations. Several performance optimizations are introduced, including advanced common sub-expressions elimination, tiling and parallelization. Some of these are obtained through well-established stencil optimizers, integrated in the back-end of the Devito compiler. The architecture of the Devito compiler, as well as the performance optimizations that are applied when generating code, are presented. The effectiveness of such performance optimizations is demonstrated using operators drawn from seismic imaging applications.

cs.MS↗

arXiv2019

Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical exploration

We introduce Devito, a new domain-specific language for implementing high-performance finite difference partial differential equation solvers. The motivating application is exploration seismology where methods such as Full-Waveform Inversion and Reverse-Time Migration are used to invert terabytes of seismic data to create images of the earth's subsurface. Even using modern supercomputers, it can take weeks to process a single seismic survey and create a useful subsurface image. The computational cost is dominated by the numerical solution of wave equations and their corresponding adjoints. Therefore, a great deal of effort is invested in aggressively optimizing the performance of these wave-equation propagators for different computer architectures. Additionally, the actual set of partial differential equations being solved and their numerical discretization is under constant innovation as increasingly realistic representations of the physics are developed, further ratcheting up the cost of practical solvers. By embedding a domain-specific language within Python and making heavy use of SymPy, a symbolic mathematics library, we make it possible to develop finite difference simulators quickly using a syntax that strongly resembles the mathematics. The Devito compiler reads this code and applies a wide range of analysis to generate highly optimized and parallel code. This approach can reduce the development time of a verified and optimized solver from months to days.

cs.DM↗

arXiv2019

Automated Tiling of Unstructured Mesh Computations with Application to Seismological Modelling

Sparse tiling is a technique to fuse loops that access common data, thus increasing data locality. Unlike traditional loop fusion or blocking, the loops may have different iteration spaces and access shared datasets through indirect memory accesses, such as A[map[i]] -- hence the name "sparse". One notable example of such loops arises in discontinuous-Galerkin finite element methods, because of the computation of numerical integrals over different domains (e.g., cells, facets). The major challenge with sparse tiling is implementation -- not only is it cumbersome to understand and synthesize, but it is also onerous to maintain and generalize, as it requires a complete rewrite of the bulk of the numerical computation. In this article, we propose an approach to extend the applicability of sparse tiling based on raising the level of abstraction. Through a sequence of compiler passes, the mathematical specification of a problem is progressively lowered, and eventually sparse-tiled C for-loops are generated. Besides automation, we advance the state-of-the-art by introducing: a revisited, more efficient sparse tiling algorithm; support for distributed-memory parallelism; a range of fine-grained optimizations for increased run-time performance; implementation in a publicly-available library, SLOPE; and an in-depth study of the performance impact in Seigen, a real-world elastic wave equation solver for seismological problems, which shows speed-ups up to 1.28x on a platform consisting of 896 Intel Broadwell cores.

cs.CE↗

arXiv2018

Long-term monitoring of the internal energy distribution of isolated cluster systems

A method is presented to monitor the internal energy distribution of cluster anions via delayed electron detachment by pulsed photoexcitation and demonstrated on Co$_4{}^-$ in an electrostatic ion beam trap. In cryogenic operation, we calibrate the detachment delay to internal energy. By laser frequency scans, at room temperature, we reconstruct the time-dependent internal energy distribution of the clusters. The mean energies of ensembles from a cold and a hot ion source both approach thermal equilibrium. Our data yield a radiative emission law and the absorptivity of the cluster for thermal radiation.

physics.atm-clus↗

arXiv2018

High-level python abstractions for optimal checkpointing in inversion problems

Inversion and PDE-constrained optimization problems often rely on solving the adjoint problem to calculate the gradient of the objec- tive function. This requires storing large amounts of intermediate data, setting a limit to the largest problem that might be solved with a given amount of memory available. Checkpointing is an approach that can reduce the amount of memory required by redoing parts of the computation instead of storing intermediate results. The Revolve checkpointing algorithm o ers an optimal schedule that trades computational cost for smaller memory footprints. Integrat- ing Revolve into a modern python HPC code and combining it with code generation is not straightforward. We present an API that makes checkpointing accessible from a DSL-based code generation environment along with some initial performance gures with a focus on seismic applications.

cs.MS↗

arXiv2017

Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age

As Ocean General Circulation Models (OGCMs) move into the petascale age, where the output from global high-resolution model runs can be of the order of hundreds of terabytes in size, tools to analyse the output of these models will need to scale up too. Lagrangian Ocean Analysis, where virtual particles are tracked through hydrodynamic fields, is an increasingly popular way to analyse OGCM output, by mapping pathways and connectivity of biotic and abiotic particulates. However, the current software stack of Lagrangian Ocean Analysis codes is not dynamic enough to cope with the increasing complexity, scale and need for customisation of use-cases. Furthermore, most community codes are developed for stand-alone use, making it a nontrivial task to integrate virtual particles at runtime of the OGCM. Here, we introduce the new Parcels code, which was designed from the ground up to be sufficiently scalable to cope with petascale computing. We highlight its API design that combines flexibility and customisation with the ability to optimise for HPC workflows, following the paradigm of domain-specific languages. Parcels is primarily written in Python, utilising the wide range of tools available in the scientific Python ecosystem, while generating low-level C-code and using Just-In-Time compilation for performance-critical computation. We show a worked-out example of its API, and validate the accuracy of the code against seven idealised test cases. This version~0.9 of Parcels is focussed on laying out the API, with future work concentrating on optimisation, efficiency and at-runtime coupling with OGCMs.

physics.ao-ph↗

arXiv2017

Optimised finite difference computation from symbolic equations

Domain-specific high-productivity environments are playing an increasingly important role in scientific computing due to the levels of abstraction and automation they provide. In this paper we introduce Devito, an open-source domain-specific framework for solving partial differential equations from symbolic problem definitions by the finite difference method. We highlight the generation and automated execution of highly optimized stencil code from only a few lines of high-level symbolic Python for a set of scientific equations, before exploring the use of Devito operators in seismic inversion problems.

cs.MS↗

arXiv2017

Experimental Determination of the Structural Coefficient of Restitution of a Bouncing Asteroid Lander

The structural coefficient of restitution describes the kinetic energy dissipation upon low-velocity (~0.1 m/s) impact of a small asteroid lander, MASCOT, against a hard, ideally elastic plane surface. It is a crucial worst-case input for mission analysis for landing MACOT on a 1km asteroid in 2018. We conducted pendulum tests and describe their analysis and the results.

physics.space-ph↗

arXiv2017

Performance prediction of finite-difference solvers for different computer architectures

The life-cycle of a partial differential equation (PDE) solver is often characterized by three development phases: the development of a stable numerical discretization, development of a correct (verified) implementation, and the optimization of the implementation for different computer architectures. Often it is only after significant time and effort has been invested that the performance bottlenecks of a PDE solver are fully understood, and the precise details varies between different computer architectures. One way to mitigate this issue is to establish a reliable performance model that allows a numerical analyst to make reliable predictions of how well a numerical method would perform on a given computer architecture, before embarking upon potentially long and expensive implementation and optimization phases. The availability of a reliable performance model also saves developer effort as it both informs the developer on what kind of optimisations are beneficial, and when the maximum expected performance has been reached and optimisation work should stop. We show how discretization of a wave equation can be theoretically studied to understand the performance limitations of the method on modern computer architectures. We focus on the roofline model, now broadly used in the high-performance computing community, which considers the achievable performance in terms of the peak memory bandwidth and peak floating point performance of a computer with respect to algorithmic choices. A first principles analysis of operational intensity for key time-stepping finite-difference algorithms is presented. With this information available at the time of algorithm design, the expected performance on target computer systems can be used as a driver for algorithm design.

cs.PF↗

arXiv2016

Anisotropic mesh adaptation in Firedrake with PETSc DMPlex

Despite decades of research in this area, mesh adaptation capabilities are still rarely found in numerical simulation software. We postulate that the primary reason for this is lack of usability. Integrating mesh adaptation into existing software is difficult as non-trivial operators, such as error metrics and interpolation operators, are required, and integrating available adaptive remeshers is not straightforward. Our approach presented here is to first integrate Pragmatic, an anisotropic mesh adaptation library, into DMPlex, a PETSc object that manages unstructured meshes and their interactions with PETSc's solvers and I/O routines. As PETSc is already widely used, this will make anisotropic mesh adaptation available to a much larger community. As a demonstration of this we describe the integration of anisotropic mesh adaptation into Firedrake, an automated Finite Element based system for the portable solution of partial differential equations which already uses PETSc solvers and I/O via DMPlex. We present a proof of concept of this integration with a three-dimensional advection test case.

math.NA↗

arXiv2016

Devito: automated fast finite difference computation

Domain specific languages have successfully been used in a variety of fields to cleanly express scientific problems as well as to simplify implementation and performance opti- mization on different computer architectures. Although a large number of stencil languages are available, finite differ- ence domain specific languages have proved challenging to design because most practical use cases require additional features that fall outside the finite difference abstraction. Inspired by the complexity of real-world seismic imaging problems, we introduce Devito, a domain specific language in which high level equations are expressed using symbolic expressions from the SymPy package. Complex equations are automatically manipulated, optimized, and translated into highly optimized C code that aims to perform compa- rably or better than hand-tuned code. All this is transpar- ent to users, who only see concise symbolic mathematical expressions.

cs.MS↗

arXiv2016

Devito: Towards a generic Finite Difference DSL using Symbolic Python

Domain specific languages (DSL) have been used in a variety of fields to express complex scientific problems in a concise manner and provide automated performance optimization for a range of computational architectures. As such DSLs provide a powerful mechanism to speed up scientific Python computation that goes beyond traditional vectorization and pre-compilation approaches, while allowing domain scientists to build applications within the comforts of the Python software ecosystem. In this paper we present Devito, a new finite difference DSL that provides optimized stencil computation from high-level problem specifications based on symbolic Python expressions. We demonstrate Devito's symbolic API and performance advantages over traditional Python acceleration methods before highlighting its use in the scientific context of seismic inversion problems.

cs.MS↗

arXiv2016

Firedrake: automating the finite element method by composing abstractions

Firedrake is a new tool for automating the numerical solution of partial differential equations. Firedrake adopts the domain-specific language for the finite element method of the FEniCS project, but with a pure Python runtime-only implementation centred on the composition of several existing and new abstractions for particular aspects of scientific computing. The result is a more complete separation of concerns which eases the incorporation of separate contributions from computer scientists, numerical analysts and application specialists. These contributions may add functionality, or improve performance. Firedrake benefits from automatically applying new optimisations. This includes factorising mixed function spaces, transforming and vectorising inner loops, and intrinsically supporting block matrix operations. Importantly, Firedrake presents a simple public API for escaping the UFL abstraction. This allows users to implement common operations that fall outside pure variational formulations, such as flux-limiters.

cs.MS↗

arXiv2016

The Cryogenic Storage Ring CSR

An electrostatic cryogenic storage ring, CSR, for beams of anions and cations with up to 300 keV kinetic energy per unit charge has been designed, constructed and put into operation. With a circumference of 35 m, the ion-beam vacuum chambers and all beam optics are in a cryostat and cooled by a closed-cycle liquid helium system. At temperatures as low as (5.5 $\pm$ 1) K inside the ring, storage time constants of several minutes up to almost an hour were observed for atomic and molecular, anion and cation beams at an energy of 60 keV. The ion-beam intensity, energy-dependent closed-orbit shifts (dispersion) and the focusing properties of the machine were studied by a system of capacitive pickups. The Schottky-noise spectrum of the stored ions revealed a broadening of the momentum distribution on a time scale of 1000 s. Photodetachment of stored anions was used in the beam lifetime measurements. The detachment rate by anion collisions with residual-gas molecules was found to be extremely low. A residual-gas density below 140 cm$^{-3}$ is derived, equivalent to a room-temperature pressure below 10$^{-14}$ mbar. Fast atomic, molecular and cluster ion beams stored for long periods of time in a cryogenic environment will allow experiments on collision- and radiation-induced fragmentation processes of ions in known internal quantum states with merged and crossed photon and particle beams.

physics.atom-ph↗