arXiv subjects

Matthias Möller

Publications and source records attributed to Matthias Möller.

At least 19 recordsLinked to original sources

arXiv2026

Parameterization-driven arbitrary Lagrangian-Eulerian method for large-deformation isogeometric fluid-structure interaction

Body-fitted arbitrary Lagrangian-Eulerian (ALE) methods provide a sharp representation of the fluid-structure interface but rely on mesh-update strategies that incrementally deform a reference configuration. To address this issue, we reformulate the ALE mesh-motion problem in the isogeometric setting as a sequence of independent domain parameterization problems. At each time step, a multi-patch spline parameterization of the fluid domain is constructed from the current interface geometry. Three technical components realize this framework: (i) a barrier-function-based spline parameterization that enforces a strictly positive Jacobian at every time step; (ii) a tangential-slip reparameterization that handles unbounded cumulative rotations of closed domains, where no fixed boundary-to-parameter correspondence is admissible; and (iii) a constant-preserving quasi-interpolation operator for solution transfer between consecutive parameterizations, ensuring that the discrete geometric conservation law holds algebraically. We validate the method on three two-dimensional FSI benchmarks, covering standard and large-rotation regimes, and on a three-dimensional rotor problem. On a rotating-square benchmark, the tangential-slip strategy enables simulations under sustained rotation far beyond the range accessible to classical mesh-update schemes--a regime that is fundamentally inaccessible to any mesh-deformation formulation, not merely numerically difficult. A three-dimensional rotor example further demonstrates that the framework extends naturally to volumetric spline parameterizations. Finally, we show that the per-step spline parameterizations can be used directly within a standard finite element solver.

math.NA↗

arXiv2026

IGA-LBM: Isogeometric lattice Boltzmann method

The lattice Boltzmann method (LBM) offers intrinsic parallelism and simple boundary treatment, but Cartesian lattices approximate curved boundaries by stair steps, causing spurious forces and boundary-layer errors. We present an isogeometric collocation lattice Boltzmann method (IGA-LBM) that solves the discrete-velocity BGK system in strong form on body-fitted B-spline and NURBS geometries. Distribution functions are collocated at Greville points, advection is evaluated with high-order operators on the exact spline mapping, and time integration uses a four-stage Runge-Kutta scheme. Exact geometric metrics preserve a uniform free stream to machine precision on curved grids while retaining formal accuracy. The analysis recovers the incompressible Navier-Stokes equations with viscosity nu = cs^2 tau and shows that the main isogeometric advantage is exact geometry rather than a new interior stencil. Centered collocation is stabilized by a high-order filter, a physical-node closure restores fourth-order accuracy near clustered walls, and stiff collision limits the explicit step to dt <= 2.785 tau, scaling as O(Re^-1). Tests confirm the predicted accuracy and the O(Ma^2) compressibility floor. Validation covers the Ghia lid-driven cavity, steady and unsteady flow past a circular cylinder, and a body-fitted NACA0012 airfoil. For the cylinder, recirculation length is within about 9 percent and the Strouhal number within about 2.5 percent of reference values; drag is 6-12 percent above consensus and decreases with refinement, with stable wakes up to Re = 1000. For NACA0012 at Re = 500 and 10 degrees incidence, surface pressure and skin friction agree with reference data within a few percent. The method provides a high-order, free-stream-preserving LBM for curved-boundary flows based on exact B-spline and NURBS geometry, with demonstrated accuracy, stability, and geometric fidelity.

math.NA↗

arXiv2026

Reduced basis algorithm for solving nonlinear differential equations on quantum computers

As quantum computing moves toward scientific computing applications, nonlinear differential equations remain a central challenge since quantum evolution is intrinsically linear. In this work, we introduce a reduced basis algorithm (RBA) for polynomial nonlinear ordinary differential equations (ODEs) and spatially discretized partial differential equations (PDEs). After time discretization, the method composes the resulting polynomial update map over $m$ timesteps, identifies the reduced monomial basis appearing in this composed map, and constructs a linear RBA operator whose action recovers the exact $m$-timestep nonlinear dynamics. Thus, at the level of the chosen discrete update rule, the method introduces no additional approximation error beyond the time discretization error. The qubit number requirement is governed by the size of the reduced monomial basis. For an $n$-dimensional polynomial ODE system of degree $p>1$, the lifted register requires at most $q_m^{\mathrm{ODE}} = O(nm\log p)$ qubits in the full basis scenario. For PDEs discretized on $N^D$ grid points, a locality-based construction requires at most $q_m^{\mathrm{PDE}} = O(D\log N + n m^{D+1}\log p)$ qubits. Hence, the dependence on the grid size remains logarithmic, while the nonlinear overhead is controlled by local reduced basis size. The main computational burden is moved from the quantum computer to a classical preprocessing step, where the reduced monomial basis and RBA operator are constructed for the chosen timestep window. Through numerical tests on the Lorenz system and the one-dimensional Burgers equation, we verify that the RBA reproduces the corresponding discrete time nonlinear dynamics exactly, while exposing the trade-off between timestep composition, reduced basis growth, and locality.

math.NA↗

arXiv2026

Efficient and Expressive Boundary Conditions in Quantum Lattice Boltzmann Methods

Quantum Lattice Boltzmann Methods (QLBM) have emerged as a promising candidate for quantum realizations of computational fluid dynamics solvers. However, despite intensive research into the QLBM in recent years, methods for imposing boundary conditions remain limited both in terms of efficiency and expressivity. In this work, we introduce a new method for imposing simple boundary conditions on QLBM that overcomes several limitations of current approaches. Our method forgoes the partitioning of the solid domain into segments and instead applies a single, coherent operation on the entire boundary. We show that our method requires fewer resources both asymptotically and practically for bounce-back and specular reflection boundary conditions.

quant-ph↗

arXiv2026

Deflation-based preconditioning for immersed finite element methods and immersogeometric analysis

Trimming is a ubiquitous operation in computer-aided-design whereby parts of a geometry are merged, intersected, or simply discarded. While it grants virtually unlimited flexibility in geometric design, it introduces a plethora of other difficulties when such geometries are used within immersed finite element methods. In particular, small cut elements lead to severely ill-conditioned system matrices requiring dedicated penalization, stabilization, or preconditioning techniques. In this work, we highlight the limitations of existing preconditioning strategies by first carefully examining the condition number of the diagonally scaled matrix and later providing realistic counter-examples for some well-established preconditioning strategies. Building on those insights, we propose a robust deflation-based preconditioning technique tailored to immersed finite element methods.

math.NA↗

arXiv2026

Quantum Lattice Boltzmann with Denoising Collision Operators

The Lattice Boltzmann method (LBM) is a well-established mesoscopic approach for simulating fluid dynamics by evolving particle distribution functions on discrete lattices. While the LBM is highly parallelizable on classical hardware, its translation to quantum algorithms is impeded by the collision process, which is intrinsically nonlinear and irreversible. Several existing quantum formulations implement this process through repeated quantum tomography and state preparation at every timestep, leading to significant overheads. We introduce a quantum LBM based on a denoising-type collision operator that avoids tomography-based updates. The collision dynamics are reformulated as an orthogonal projection onto the linearized manifold of equilibrium distributions around a reference state. This geometric approach filters non-equilibrium components while preserving lattice symmetries and approximating nonlinear terms needed to recover hydrodynamic behavior. A complete pipeline is presented with efficient gate-level realizations, incorporating encoding of distributions, collision, streaming, boundary conditions, and measurement of physical quantities such as hydrodynamic forces. In addition, we outline an approach for implementing projector-based operators deterministically without postselection, paving the way to fully coherent multi-timestep LBM simulations. Numerical experiments for advection-diffusion and flow problems demonstrate that the method reproduces macroscopic behaviors with high accuracy, with performance depending on the choice of reference state.

quant-ph↗

arXiv2025

Fully Quantum Lattice Gas Automata Building Blocks for Computational Basis State Encodings

Lattice Gas Automata (LGA) is a classical method for simulating physical phenomena, including Computational Fluid Dynamics (CFD). Quantum LGA (QLGA) is the family of methods that implement LGA schemes on quantum computers. In recent years, QLGA has garnered attention from researchers thanks to its potential of efficiently modeling CFD processes by either reducing memory requirements or providing simultaneous representations of exponentially many LGA states. In this work, we introduce novel building blocks for QLGA algorithms that rely on computational basis state encodings. We address every step of the algorithm, from initial conditions to measurement, and provide detailed complexity analyses that account for all discretization choices of the system under simulation. We introduce multiple ways of instantiating initial conditions, efficient boundary condition implementations for novel geometrical patterns, a novel collision operator that models less restricted interactions than previous implementations, and quantum circuits that extract quantities of interest out of the quantum state. For each building block, we provide intuitive examples and open-source implementations of the underlying quantum circuits.

quant-ph↗

arXiv2025

Efficient thermal simulation in metal additive manufacturing via semi-analytical isogeometric analysis

Thermal modeling of Laser Powder Bed Fusion (LPBF) is challenging due to steep, rapidly moving thermal gradients induced by the laser, which are difficult to resolve accurately with conventional Finite Element Methods. Highly refined, dynamically adaptive spatial discretization is typically required, leading to prohibitive computational costs. Semi-analytical approaches mitigate this by decomposing the temperature field into an analytical point-source solution and a complementary numerical field that enforces boundary conditions. However, state-of-the-art implementations either necessitate extensive mesh refinement near boundaries or rely on restrictive image source techniques, limiting their efficiency and applicability to complex geometries. This study presents a novel reformulation of the semi-analytical framework using Isogeometric Analysis. The laser heat input is captured by the analytical point-source solution, while the complementary correction field, which imposes boundary conditions, is solved using a spline-based IGA discretization. The governing heat equation for the correction field is cast in a weak form, discretized with NURBS basis functions, and advanced in time using an implicit $θ$-scheme. This approach leverages IGA's key advantages: exact geometry representation, higher-order continuity, and superior accuracy per degree of freedom. These features unlock efficient thermal modeling of realistic parts with complex contours. Our strategy eliminates the need for scan-wise remeshing and robustly handles intricate geometric features like sharp corners and varying cross-sections. Numerical examples demonstrate that the proposed semi-analytical IGA method delivers accurate temperature predictions and achieves substantial computational efficiency gains compared to standard FEM, establishing it as a powerful new tool for high-fidelity thermal simulation in LPBF.

math.NA↗

arXiv2025

Surrogate Quantum Circuit Design for the Lattice Boltzmann Collision Operator

This study introduces a framework for learning a low-depth surrogate quantum circuit (SQC) that approximates the nonlinear, dissipative, and hence non-unitary Bhatnagar-Gross-Krook (BGK) collision operator in the lattice Boltzmann method (LBM) for the D2Q9 lattice. By appropriately selecting the quantum state encoding, circuit architecture, and measurement protocol, non-unitary dynamics emerge naturally within the physical population space. This approach removes the need for probabilistic algorithms relying on any ancilla qubits and post-selection to reproduce dissipation, or for multiple state copies to capture nonlinearity. The SQC is designed to preserve key physical properties of the BGK operator, including mass conservation, scale equivariance, and D8 equivariance, while momentum conservation is encouraged through penalization in the training loss. When compiled to the IBM Heron quantum processor's native gate set, assuming all-to-all qubit connectivity, the circuit requires only 724 native gates and operates locally on the velocity register, making it independent of the lattice size. The learned SQC is validated on two benchmark cases, the Taylor-Green vortex decay and the lid-driven cavity, showing accurate reproduction of vortex decay and flow recirculation. While integration of the SQC into a quantum LBM framework presently requires measurement and re-initialization at each timestep, the necessary steps towards a measurement-free formulation are outlined.

quant-ph↗

arXiv2025

Quantum Search in Superposed Quantum Lattice Gas Automata and Lattice Boltzmann Systems

As the scope of Computational Fluid Dynamics (CFD) grows to encompass ever larger problem scales, so does the interest in whether quantum computing can provide an advantage. In recent years, Quantum Lattice Gas Automata (QLGA) and Quantum Lattice Boltzmann Methods (QLBM) have emerged as promising candidates for quantum-native implementations of CFD solvers. Though the progress in developing QLGA and QLBM algorithms has been significant, it has largely focused on the development of models rather than applications. As a result, the zoo of QLGA and QLBM algorithms has grown to target several equations and to support many extensions, but the practical use of these models is largely limited to quantum state tomography and observable measurement. This limitation is crucial in practice, because unless very specific criteria are met, such measurements may cancel out any potential quantum advantage. In this paper, we propose an application based on discrete optimization and quantum search, which circumvents flow field measurement altogether. We propose methods for simulating many different lattice configurations simultaneously and describe how the usage of amplitude estimation and quantum search can provide an asymptotic quantum advantage. Throughout the paper, we provide detailed complexity analyses of gate-level implementations of our circuits and consider the benefits and costs of several encodings.

quant-ph↗

arXiv2025

Isogeometric multi-patch shell analysis using the Geometry + Simulation Modules

Isogeometric Analysis (IGA) bridges Computer-Aided Design (CAD) and Finite Element Analysis (FEA) by employing splines as a common basis for geometry and analysis. One of the advantages of IGA is in the realm of thin shell analysis: due to the arbitrary continuity of the spline basis, Kirchhoff-Love shells can be modeled without the need to introduce unknowns for the mid-plane rotations, leading to a reduction in the number of unknowns. In this paper, we provide the background of an implementation of Isogeometric Kirchhoff--Love shells within the Geometry + Simulation Modules (G+Smo). This paper accompanies multiple previous publications and elaborates on the design of the software used in these papers, rather than the novelty of the methods presented therein. The presented implementation provides patch coupling via penalty methods and unstructured splines, goal-oriented error estimators, several algorithms for structural analysis and advanced algorithms for the modeling of wrinkling in hyperelastic membranes. These methods are all contained in three new modules in G+Smo: a module for Kirchhoff-Love shells, a module for structural analysis, and a module for unstructured spline constructions. As motivated in this paper, the modules are implemented to be compatible with future developments. For example, by providing base implementations of material laws, by using black-box functions for the structural analysis module, or by providing a standardized approach for the implementation of unstructured spline constructions. Overall, this paper demonstrates that the new modules contribute to a versatile ecosystem for the modeling of multi-patch shell problems through fast off-the-shelf solvers with a simple interface, designed to be extended in future research.

math.NA↗

arXiv2025

qlbm -- A Quantum Lattice Boltzmann Software Framework

We present qlbm, a Python software package designed to facilitate the development, simulation, and analysis of Quantum Lattice Boltzmann Methods (QBMs). qlbm is a modular framework that introduces a quantum component abstraction hierarchy tailored to the implementation of novel QBMs. The framework interfaces with state-of-the-art quantum software infrastructure to enable efficient simulation and validation pipelines, and leverages novel execution and pre-processing techniques that significantly reduce the computational resources required to develop quantum circuits. We demonstrate the versatility of the software by showcasing multiple QBMs in 2D and 3D with complex boundary conditions, integrated within automated benchmarking utilities. Accompanying the source code are extensive test suites, thorough online documentation resources, analysis tools, visualization methods, and demos that aim to increase the accessibility of QBMs while encouraging reproducibility and collaboration. The source code of qlbm is publicly available under a permissive MPL 2.0 license at https://github.com/QCFD-Lab/qlbm.

quant-ph↗

arXiv2025

IsoGeometric Suitable Coupling Methods for Partitioned Multiphysics Simulation with Application to Fluid-Structure Interaction

This paper presents spline-based coupling methods for partitioned multiphysics simulations, specifically designed for isogeometric analysis (IGA) based solvers. Traditional vertex-based coupling approaches face significant challenges when applied to IGA solvers, including geometric accuracy issues, interpolation errors, and substantial communication overhead. The methodology draws on the IGA mathematical framework to deliver coupling solutions that preserve high-order continuity and exact geometric representation of splines. We develop two complementary strategies: (1) a spline-vertex coupling method enabling efficient interaction between IGA and conventional solvers, and (2) a fully isogeometric coupling approach maximizing accuracy for IGA-to-IGA communication. Both theoretical analysis and extensive numerical experiments demonstrate that our spline-based methods significantly reduce communication overhead compared to traditional approaches while enhancing geometric accuracy through exact boundary representation and maintaining higher-order solution continuity across coupled interfaces. We quantitatively confirm communication efficiency benefits through systematic measurements of transfer times and data volumes across various mesh refinement levels. Our benchmark studies demonstrate geometric fidelity advantages while highlighting how splines naturally preserve solution derivatives across interfaces without requiring additional computation. This work provides efficient coupling strategies tailored to IGA-based solvers and establishes a practical bridge between IGA and traditional discretization methods, enabling broader adoption of IGA in established simulation workflows.

math.NA↗

arXiv2025

Neurosymbolic Decision Trees

Neurosymbolic (NeSy) AI studies the integration of neural networks (NNs) and symbolic reasoning based on logic. Usually, NeSy techniques focus on learning the neural, probabilistic and/or fuzzy parameters of NeSy models. Learning the symbolic or logical structure of such models has, so far, received less attention. We introduce neurosymbolic decision trees (NDTs), as an extension of decision trees together with a novel NeSy structure learning algorithm, which we dub NeuID3. NeuID3 adapts the standard top-down induction of decision tree algorithms and combines it with a neural probabilistic logic representation, inherited from the DeepProbLog family of models. The key advantage of learning NDTs with NeuID3 is the support of both symbolic and subsymbolic data (such as images), and that they can exploit background knowledge during the induction of the tree structure, In our experimental evaluation we demonstrate the benefits of NeSys structure learning over more traditonal approaches such as purely data-driven learning with neural networks.

cs.AI↗

arXiv2025

Momentum exchange method for quantum Boltzmann methods

The past years have seen a surge in quantum algorithms for computational fluid dynamics (CFD). These algorithms have in common that whilst promising a speed-up in the performance of the algorithm, no specific method of measurement has been suggested. This means that while the algorithms presented in the literature may be promising methods for creating the quantum state that represents the final flow field, an efficient measurement strategy is not available. This paper marks the first quantum method proposed to efficiently calculate quantities of interest (QoIs) from a state vector representing the flow field. In particular, we propose a method to calculate the force acting on an object immersed in the fluid using a quantum version of the momentum exchange method (MEM) that is commonly used in lattice Boltzmann methods to determine the drag and lift coefficients. In order to achieve this we furthermore give a scheme that implements bounce back boundary conditions on a quantum computer, as those are the boundary conditions the momentum exchange method is designed for.

quant-ph↗

arXiv2025

On the importance of data encoding in quantum Boltzmann methods

In recent years, quantum Boltzmann methods have gained more and more interest as they might provide a viable path towards solving fluid dynamics problems on quantum computers once this emerging compute technology has matured and fault-tolerant many-qubit systems become available. The major challenge in developing a start-to-end quantum algorithm for the Boltzmann equation consists in encoding relevant data efficiently in quantum bits (qubits) and formulating the streaming, collision and reflection steps as one comprehensive unitary operation. The current literature on quantum Boltzmann methods mostly proposes data encodings and quantum primitives for individual phases of the pipeline assuming that they can be combined to a full algorithm. In this paper we disprove this assumption by showing that for encodings commonly discussed in literature either the collision or the streaming step cannot be unitary. Building on this landmark result we propose a novel encoding in which the number of qubits used to encode the velocity depends on the number of time steps one wishes to simulate, with the upper bound depending on the total number of grid points. In light of the non-unitarity result established for existing encodings, our encoding method is to the best of our knowledge the only one currently known that can be used for a start-to-end quantum Boltzmann solver where both the collision and the streaming step are implemented as a unitary operation. Furthermore our theoretical unitarity result can serve as a guideline on which types of encodings to consider or whether a `stop-and-go' method with repeated measurements and re-initializations is the method of choice.

quant-ph↗

arXiv2025

Efficient and fail-safe quantum algorithm for the transport equation

We present a scalable algorithm for solving the transport equation in two and three spatial dimensions for variable grid sizes and discrete velocities on a fault-tolerant universal quantum computer. As a proof of concept of our quantum transport method (QTM), we describe a full-circuit start-to-end implementation in Qiskit and present numerical results for 2D flows. Our QTM is based on a novel streaming approach which leads to a reduction in the amount of CNOT gates required in comparison to state-of-the-art quantum streaming methods. As a second highlight we present a novel object encoding method, that reduces the complexity of the amount of CNOT gates required to encode walls, which now becomes independent of the size of the wall. Finally we present a novel quantum encoding of the particles' discrete velocities that enables a linear speed-up in the costs of reflecting the velocity of a particle, which now becomes independent of the amount of velocities encoded. Our main contribution is a detailed description of a fail-safe implementation of a quantum algorithm for the reflection step of the transport equation that can be readily implemented on a physical quantum computer. This fail-safe implementation allows for a variety of initial conditions and particle velocities and leads to physically correct behavior around the walls, edges and corners of obstacles. Combining these results we present a novel and fail-safe start-to-end quantum algorithm for the transport equation that can be used for a multitude of flow configurations. We finally show that our approach is quadratic in the amount of qubits necessary to encode the grid and the amount of qubits necessary to encode the discrete velocities in a single spatial dimension, which makes our approach superior to state-of-the-art approaches known in the literature.

quant-ph↗

arXiv2024

Solving 1D Poisson problem with a Variational Quantum Linear Solver

Different hybrid quantum-classical algorithms have recently been developed as a near-term way to solve linear systems of equations on quantum devices. However, the focus has so far been mostly on the methods, rather than the problems that they need to tackle. In fact, these algorithms have been run on real hardware only for problems in quantum physics, such as Hamiltonians of a few qubits systems. These problems are particularly favorable for quantum hardware, since their matrices are the sum of just a few unitary terms and since only shallow quantum circuits are required to estimate the cost function. However, for many interesting problems in linear algebra, it appears far less trivial to find an efficient decomposition and to trade it off with the depth of the cost quantum circuits. A first simple yet interesting instance to consider are tridiagonal systems of equations. These arise, for instance, in the discretization of one-dimensional finite element analyses. This work presents a method to solve a class of tridiagonal systems of equations with the variational quantum linear solver (VQLS), a recently proposed variational hybrid algorithm for solving linear systems. In particular, we present a new decomposition for this class of matrices based on both Pauli strings and multi--qubit gates, resulting in less terms than those obtained by just using Pauli gates. Based on this decomposition, we discuss the tradeoff between the number of terms and the near-term implementability of the quantum circuits. Furthermore, we present the first simulated and real-hardware results obtained by solving tridiagonal linear systems with VQLS, using the decomposition proposed.

cs.CE↗