26 results
Now showing1 - 10 of 26
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Operator learning for efficient quantum computation(IOP Publishing, 2026-10-27); ; ; ; An efficient implementation of quantum algorithms is often hindered by the lack of efficient primitives for operators and state preparation. This limits both the ability of near-term quantum hardware to simulate complex problems and the potential of fault-tolerant algorithms to achieve practical quantum advantage. To address this, we propose a framework that maps discrete operators onto compact, structured quantum circuits. The resulting variational circuits can be tailored to the connectivity and long-range interaction of the target hardware. The learning process employs backpropagation together with a cost function that efficiently optimizes unitary operators and non-unitary --dense or sparse-- operators using only a single ancilla qubit for block encoding. Additionally, we introduce a regularization term that reduces the approximation error. The approach is validated for both quantum mechanical and engineering applications. In the former case, we learn propagators that arise in native quantum problems --such as quantum simulation and quantum chemistry-- and achieve improved resource scaling in comparison to standard Suzuki--Trotter expansions. In the latter case, we demonstrate the approach's ability to implement the second-order central finite difference approximation of the Laplace operator --relevant for solving partial differential equations-- while improving upon current error metrics. The final example deals with learning a dense, non-unitary operator that arises in the analysis of inviscid potential flow around an airfoil. These results motivate the extension of the framework to a broader class of operator-synthesis problems beyond the prototypical engineering and quantum applications considered here.Publicationtype: Journal Article73 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Conditional neural field-based reduced order model for dynamic ditching load prediction(American Institute of Physics (AIP), 2026-10-22); ; ; Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dynamics. Recently, the use of coordinate-based approaches like conditional neural fields (CNFs) has emerged. Their independence of the spatial discretization is an advantageous feature and a key motivation for computational fluid dynamic applications. This paper discusses the spatiotemporal prediction of aircraft ditching loads using a CNF approach. The model employs a Fourier feature mapping of input coordinates to capture high frequency content. Based on feature-wise linear modulation, the output is conditioned on a latent vector, which is learned by autodecoding. The model is evaluated using two datasets for the dynamic loads on the fuselage of a German Aerospace Center (DLR) D150 aircraft. The first dataset relates to a single fixed spatial discretization, and the second dataset includes data from different discretizations. On the first dataset, the model is compared to a grid-dependent convolutional autoencoder-based model, and both approaches achieve similar reconstruction accuracy, but the CNF requires significantly fewer parameters. When paired with a long short-term memory network in the latent space, both approaches achieve similar spatiotemporal prediction accuracy. Results for the second dataset demonstrate the ability of the CNF-based approach to reconstruct ditching loads accurately for heterogeneous spatial discretizations. In particular, the model can generalize to discretizations not used for training. This allows for flexible use of training datasets generated for different geometries and/or discretizations and the use of the surrogate model to predict loads for different configurations.Publicationtype: Journal Article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Quantum-inspired tensor-network fractional-step method for incompressible flow in curvilinear coordinates(Elsevier, 2026-04-22); ; ; ; We introduce an algorithmic framework based on tensor networks for computing fluid flows around immersed objects in curvilinear coordinates. We show that the tensor network simulations can be carried out solely using highly compressed tensor representations of the flow fields and the differential operators and discuss the numerical implementation of the tensor operations required for computing fluid flows in detail. The applicability of our method is demonstrated by applying it to the paradigm example of steady and transient flows around stationary and rotating cylinders. We find excellent quantitative agreement in comparison to finite difference simulations for Strouhal numbers, forces and velocity fields. The properties of our approach are discussed in terms of reduced order models. We estimate the memory saving and potential runtime advantages in comparison to standard finite difference simulations. We find accurate results with errors of less than 0.3% for flow-field compressions by a factor of up to 20 and differential operators compressed by factors of up to 1000 compared to sparse matrix representations. We provide strong numerical evidence that the runtime scaling advantages of the tensor network approach with system size will provide substantial resource savings when simulating larger systems. Finally, we note that, like other tensor network-based fluid flow simulations, our algorithmic framework is directly portable to a quantum computer leading to further scaling advantages.Publicationtype: Journal Article47 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Quantum time‐marching algorithms for solving linear transport problems including boundary conditionsThis article presents the first complete application of a quantum time‐marching algorithm for simulating multidimensional linear transport phenomena with arbitrary boundaries, whereby the success probabilities are problem intrinsic. The method adapts the linear combination of unitaries algorithm to block encode the diffusive dynamics, while arbitrary boundary conditions are enforced by the method of images only at the cost of one additional qubit per spatial dimension. As an alternative to the nonperiodic reflection, the direct encoding of Neumann conditions by the unitary decomposition of the discrete time‐marching operator is proposed. All presented algorithms indicate optimal success probabilities while maintaining linear time complexity, thereby securing the practical applicability of the quantum algorithm on fault‐tolerant quantum computers. The proposed time‐marching method is demonstrated through state‐vector simulations of the heat equation in combination with Neumann, Dirichlet, and mixed boundary conditions, showing excellent agreement with classical finite differences.Publicationtype: Journal Article24 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Toward variational quantum algorithms for generalized linear and nonlinear transport phenomena(American Institute of Aeronautics and Astronautics (AIAA), 2026-02); ; ; This article proposes a variational quantum algorithm to solve linear and nonlinear thermofluid dynamic transport equations. The hybrid classical-quantum framework is applied to problems governed by the heat, wave, and Burgers’ equations in combination with different engineering boundary conditions. Topics covered include the encoding of band matrices, as in the consideration of nonconstant material properties and upwind-biased first- and higher-order approximations, widely used in engineering computational fluid dynamics, by the use of a mask function. Verification examples demonstrate high predictive agreement with classical methods. Furthermore, the scalability analysis shows a polylog scaling of the number of quantum gates with the number of qubits. Remaining challenges refer to the implicit construction of upwind schemes and the identification of an appropriate parameterization strategy of the quantum ansatz.Publicationtype: Journal Article66 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Disentangled latent spaces for reduced order models using deterministic autoencodersData-driven reduced-order models based on autoencoders generally lack interpretability compared to classical methods such as the proper orthogonal decomposition. More interpretability can be gained by disentangling the latent variables and analyzing the resulting modes. For this purpose, probabilistic β-variational autoencoders (β-VAEs) are frequently used in computational fluid dynamics and other simulation sciences. Using a benchmark periodic flow dataset, we show that competitive results can be achieved using non-probabilistic autoencoder approaches that either promote orthogonality or penalize correlation between latent variables. Compared to probabilistic autoencoders, these approaches offer more robustness with respect to the choice of hyperparameters entering the loss function. We further demonstrate the ability of a non-probabilistic approach to identify a reduced number of active latent variables by introducing a correlation penalty, a function also known from the use of β-VAE. The investigated probabilistic and non-probabilistic autoencoder models are finally used for the dimensionality reduction of aircraft ditching loads, which serves as an industrial application in this work.Publicationtype: Journal Article93 44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Data-driven pressure field prediction for ships in regular sea statesMerchant shipping is responsible for more than 90% of the global trade and has a significant environmental impact, accounting for over 2% of global greenhouse gas emissions. Therefore, fuel-saving measures are becoming increasingly important in reducing the ecological footprint and increasing the fuel efficiency of maritime transport. Routing optimization systems, which require a rapid prediction of ambient-dependent fuel consumption, represent an essential pillar here, e.g. to reduce added resistances due to seaways and/or wind. The paper aims to predict the added resistance due to seaways. In contrast to conventional methods the goal is achieved by surrogate modelling of the entire pressure fields. To this end, an online/offline-procedure is applied to an exemplarily free-floating container vessel. The online approach to be trained consists of two building blocks, namely a convolutional autoencoder (CAE)-based order reduction step and a neural network-based (NN) regression step that links the reduced space of the autoencoder with three control parameters that describe the sea state (wave height/steepness, encounter angle and wave length). Training data is obtained from time-averaged values for simulating instantaneous ship motion and pressure fields. During the offline phase, the combined CAE/NN is trained to capture the time-averaged pressure fields for a variety of sea-state conditions. During ship operation (online phase), the surrogate model predicts the three-dimensional pressure fields in response to sea state conditions, projects the pressure fields onto the ship hull, and integrates the corresponding resistances to guide the route. The evaluation of the method shows promising results for the different building blocks and the concept could therefore represent an attractive approach for cost-effective surrogate modelling of complex multiphase flow fields.Publicationtype: Journal Article68 47 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Quantum algorithm for the advection-diffusion equation by direct block encoding of the time-marching operator(American Physical Society (APS), 2025-07-01); ; ; ; A quantum algorithm for simulating multidimensional scalar transport problems using a time-marching strategy is presented. A direct unitary block encoding of the explicit time-marching operator is constructed, resulting in the intrinsic success probability of the squared solution norm without the need for amplitude amplification, thereby retaining a linear dependence on the simulation time. The algorithm separates the explicit time-marching operator into an advection-like component and a corrective shift operator. The advection-like component is mapped to a Hamiltonian simulation and combined with the shift operator through the linear combination of unitaries algorithm. State-vector simulations of a scalar transported in a steady two-dimensional Taylor-Green vortex support the theoretical findings.Publicationtype: Journal Article56 51 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Monolithic 3D numerical modeling of granular cargo movement on bulk carriers in wavesA novel monolithic approach for simulating vessels in waves with granular cargo is presented using a Finite Volume framework. The integrated approach aims to develop a tool for maritime safety analysis and design optimization of bulk carriers. The computational model integrates a three-phase Volume of Fluid method to represent air, water, and cargo, coupled with a material model that applies a rigid-perfectly plastic material model for the granular phase. The approach takes into account the floating motion of the ship using a rigid body motion solver for three degrees of freedom and is supplemented by inviscid far-field boundary conditions facilitating the generation of linear waves approaching the vessel. The model's efficacy is demonstrated through a validation of the three-phase Volume of the Fluid method, a verification of the granular material model, and finally, a reconstruction of the accident of the “Jian Fu Star” bulk carrier in a fully 3D simulation.Publicationtype: Journal Article23 33 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Adjoint-assisted robust shape optimization of an idealized arterial bypass graft using the FOSM methodThis paper presents the shape optimization of an idealized arterial bypass graft under uncertainties. The underlying blood flow problem is numerically solved using computational fluid dynamics (CFD) simulations, able to account for mechanical hemolysis and non-Newtonian viscosity properties of blood. The employed hemolysis and non-Newtonian models utilize a set of parameters that are considered uncertain in this work. To this end, the optimization problem is rendered robust and targets the minimization of the first two statistical moments of a hemolysis index. The propagation of the uncertainties to the hemolysis index is done through the first-order second-moment method (FOSM). The necessary derivatives are computed by the adjoint method and finite differences. Several steady-state robust shape optimization simulations of the idealized bypass graft are presented. Selected optimized shapes are further assessed by means of additional fluid–structure interaction (FSI) simulations under unsteady conditions.Publicationtype: Journal Article28 21