arXiv subjects

Ole Sigmund

Publications and source records attributed to Ole Sigmund.

At least 19 recordsLinked to original sources

arXiv2026

Sensitivity Hot Spot Penalization: A Robust Topology Optimization Framework against First-Order Worst-Case Perturbations

Deterministic topology optimization can efficiently generate high-performance structural designs, but it does not explicitly control localized fragility induced by manufacturing variations and geometric uncertainties. Such fragility often appears as stress concentrations or hinge-like deformation mechanisms. Conventional robust topology optimization can suppress these features, but typically requires multiple design realizations and substantially increased computational cost. Recent work by Sigmund et al. (2026) introduced sensitivity hot spot penalization (SHoSP), which augments the nominal objective by a smooth maximum of its sensitivities and suppresses localized fragile features at low additional cost while promoting more even stress distributions. This paper establishes a general connection between SHoSP and a first-order worst-case robust approximation under a material-mass perturbation budget using a Taylor expansion and H{"o}lder's duality. The original penalty weight is identified as a dimensionless norm-bounded perturbation budget relative to the area (2D) or volume (3D) of the design domain. This interpretation explains the suppression of sensitivity hot spots, hinge localization, and stress concentrations by limiting the maximum first-order degradation under budgeted perturbations. It is investigated for compliance minimization and compliant mechanism design. Robustness is assessed using a relaxed worst-case density perturbation and by comparing deterministic and SHoSP designs under equivalent perturbation budgets. Stress-related effects observed on structured meshes are cross-validated by stress-oriented topology optimization and body-fitted finite element analyses. Extensions to porous infill optimization, multiple load cases, and large-scale 3D examples further demonstrate the applicability and superior scalability of the SHoSP framework.

cs.CE↗

arXiv2026

Spectrally smooth broadband response via autocorrelation-constrained inverse design

Time-domain inverse design in photonics is known to be suitable for maximizing the efficiency of optical devices over broad frequency ranges. Objectives commonly used in this context include time-integrated field quantities derived from Poynting's theorem, such as energy, flux, or dissipated power, which can be directly linked to integrated frequency-domain responses via Parseval's theorem. While computationally efficient, these objectives measure only the total response over the targeted bandwidth and, as we demonstrate, are insufficient to capture undesired in-band ripple, narrow spectral features, or sidelobes. We overcome this limitation by introducing a time-domain metric quantifying such spectral variations based on the weighted long-lag autocorrelation energy of the optical response. We incorporate this metric into an FDTD-based topology-optimization framework and demonstrate its beneficial effect on the example of inverse designing one-dimensional dielectric Bragg mirrors via the time-domain adjoint method.

physics.optics↗

arXiv2026

Novel insights into Pareto fronts in multiobjective topology optimization and a comparative study of scalarization strategies

Topology optimization seeks structures that minimize an objective function subject to constraints. While extensive research has focused on single-objective formulations, design is usually a trade-off between conflicting criteria. This naturally leads topology optimization to the multiobjective optimization field. Unfortunately, despite their long coexistence, interaction between these fields has remained limited. This work aims to bridge this gap. First, we provide a theoretical comparison of two common scalarization techniques, the weighted-sum and $\varepsilon$-constraint method, to the established Pascoletti-Serafini scalarization. Then, we perform extensive bi-objective numerical experiments, including minimization of volume, compliance, maximum stress and dynamic compliance. For each experiment, the four scalarization methods are aggregated, often alongside design space sampling and continuation, to better resolve the Pareto front. The major contribution of this work is the observation that the Pareto front consists of segments belonging to local fronts. Contrary to the smooth, convex fronts often reported in the literature, we show that this property can lead to discontinuous and nonconvex fronts. Finally, we numerically compare the approximation quality of the scalarization methods. The numerical examples demonstrate the robustness of the Pascoletti-Serafini scalarization against fronts with pronounced discontinuities, nonconvexities and high-curvature regions. In contrast, the weighted-sum and $\varepsilon$-constraint methods can produce clustering and gaps, depending on the shape of the front.

math.OC↗

arXiv2025

A Matlab code for analysis and topology optimization with Third Medium Contact

We present a Matlab code for modelling and topology optimization of hyperelastic structures, including contact modelled by the Third Medium Contact (TMC) approach. By using the so-called HuHu-regularization we penalize the skew distortion of the bilinear finite elements discretizing void regions, thus promoting convergence of the nonlinear solver. First, we show how this method is implemented in a compact code, allowing to simulate contact and force transfer in hyperelastic structures. We then solve two topology optimization problems for minimum end-compliance of structures exhibiting contact. In the first example, contact happens at the supported boundary, while the second features self-contact. The Matlab scripts that replicate the results are included, and we discuss some possible extensions to more general problems.

cs.MS↗

arXiv2025

Efficient first-principles inverse design of nanolasers

We develop and demonstrate a first-principles approach, based on the nonlinear Maxwell-Bloch equations and steady-state ab-initio laser theory (SALT), for inverse design of nanostructured lasers, incorporating spatial hole-burning corrections, threshold effects, out-coupling efficiency, and gain diffusion. The resulting figure of merit exploits the high-$Q$ regime of optimized laser cavities to perturbatively simplify the nonlinear model to a single linear ''reciprocal'' Maxwell solve. The consequences for laser-cavity design, and in particular the strong dependence on the nature of the gain region, are demonstrated using topology optimization of both 2d and full 3d geometries.

physics.optics↗

arXiv2025

A 140 line MATLAB code for topology optimization problems with probabilistic parameters

We present an efficient 140 line MATLAB code for topology optimization problems that include probabilistic parameters. It is built from the top99neo code by Ferrari and Sigmund and incorporates a stochastic sample-based approach. Old gradient samples are adaptively recombined during the optimization process to obtain a gradient approximation with vanishing approximation error. The method's performance is thoroughly analyzed for several numerical examples. While we focus on applications in which stochastic parameters describe local material failure, we also present extensions of the code to other settings, such as uncertain load positions or dynamic forces of unknown frequency. The complete code is included in the Appendix and can be downloaded from www.topopt.dtu.dk.

math.OC↗

arXiv2025

Optimization of the initial post-buckling response of trusses and frames by an asymptotic approach

Asymptotic post-buckling theory is applied to sizing and topology optimization of trusses and frames, exploring its potential and current computational difficulties. We show that a designs' post-buckling response can be controlled by including the lowest two asymptotic coefficients, representing the initial post-buckling slope and curvature, in the optimization formulation. This also reduces the imperfection sensitivity of the optimized design. The asymptotic expansion can further be used to approximate the structural nonlinear response, and then to optimize for a given measure of the nonlinear mechanical performance such as, for example, end-compliance or complementary work. Examples of linear and nonlinear compliance minimization of trusses and frames show the effective use of the asymptotic method for including post-buckling constraints in structural optimization.

cs.CE↗

arXiv2025

Yield and Buckling Stress Limits in Topology Optimization of Multiscale Structures

This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process. Building upon established multiscale methodologies, we develop a new framework incorporating yield stress limits either as constraints or objectives alongside previously established local and global buckling constraints. This approach significantly refines the optimization process, ensuring that the resulting designs meet mechanical performance criteria and adhere to critical material yield constraints. First, we establish local density-dependent von Mises yield surfaces based on local yield estimates from homogenization-based analysis to predict the local yield limits of the homogenized materials. Then, these local Yield-based Load Factors (YLFs) are combined with local and global buckling criteria to obtain topology optimized designs that consider yield and buckling failure on all levels. This integration is crucial for the practical application of optimized structures in real-world scenarios, where material yield and stability behavior critically influence structural integrity and durability. Numerical examples demonstrate how optimized designs depend on the stiffness to yield ratio of the considered building material. Despite the foundational assumption of separation of scales, the de-homogenized structures, even at relatively coarse length scales, exhibit a high degree of agreement with the corresponding homogenized predictions.

cs.CE↗

arXiv2025

A practical review on promoting connectivity in topology optimization

Topology optimization facilitates the automated design of high-performance structures across various engineering fields but, if unconstrained, often produces designs that are complex and difficult to manufacture. A key attribute of the resulting designs is connectivity, which involves controlling the presence of solid and/or void islands of material. This manuscript provides a comprehensive overview of existing connectivity constraints developed for continuous design representations and highlights their advantages and limitations in influencing design outcomes and performance. The review further includes a practical comparison of five different connectivity constraints using a topology optimization framework for sandwich panels that balances acoustic and structural performance. With Pareto-front analyses, the constraints are evaluated based on computational cost, monotonicity, parameter dependency, and their impact on the optimized designs, their performance, and underlying dynamics. From the comparison, practical insights and rule of thumbs have been derived. The findings emphasize the critical role of selecting appropriate connectivity constraints, given their significant effect on the optimization results.

physics.app-ph↗

arXiv2024

Topology optimization of high-performance optomechanical resonator

High quality mechanical resonators are critical for driving advances in quantum information technologies, precision sensing, and optomechanics. However, achieving compact resonator designs that maintain high performance is a key challenge. In this study, we present a new class of compact resonators optimized to operate at higher-order eigenmodes, achieving both high frequencies and enhanced quality factor-frequency (Qf) products. By employing topology optimization to maximize the damping dilution factor, these resonators achieve minimized edge bending losses and enhanced intrinsic damping. Their high-(Qf) performance and compact form factor position these resonators as promising candidates for applications in quantum information transduction, advanced optomechanical systems, and next-generation sensing technologies.

quant-ph↗

arXiv2024

On performance bounds for topology optimization

Topology optimization has matured to become a powerful engineering design tool that is capable of designing extraordinary structures and materials taking into account various physical phenomena. Despite the method's great advancements in recent years, several unanswered questions remain. This paper takes a step towards answering one of the larger questions, namely: How far from the global optimum is a given topology optimized design? Typically this is a hard question to answer, as almost all interesting topology optimization problems are non-convex. Unfortunately, this non-convexity implies that local minima may plague the design space, resulting in optimizers ending up in suboptimal designs. In this work, we investigate performance bounds for topology optimization via a computational framework that utilizes Lagrange duality theory. This approach provides a viable measure of how \say{close} a given design is to the global optimum for a subset of optimization formulations. The method's capabilities are exemplified via several numerical examples, including the design of mode converters and resonating plates.

cs.CE↗

arXiv2024

Engineering optical forces through Maxwell stress tensor inverse design

Precise spatial manipulation of particles via optical forces is essential in many research areas, ranging from biophysics to atomic physics. Central to this effort is the challenge of designing optical systems that are optimized for specific applications. Traditional design methods often rely on trial-and-error methods, or on models that approximate the particle as a point dipole, which only works for particles much smaller than the wavelength of the electromagnetic field. In this work, we present a general inverse design framework based on the Maxwell stress tensor formalism capable of simultaneously designing all components of the system, while being applicable to particles of arbitrary sizes and shapes. Notably, we show that with small modifications to the baseline formulation, it is possible to engineer systems capable of attracting, repelling, accelerating, oscillating, and trapping particles. We demonstrate our method using various case studies where we simultaneously design the particle and its environment, with particular focus on free-space particles and particle-metalens systems.

physics.optics↗

arXiv2024

Morphogenesis of sound creates acoustic rainbows

Sound is an essential sensing element for many organisms in nature, and multiple species have evolved organic structures that create complex acoustic scattering and dispersion phenomena to emit and perceive sound unambiguously. To date, it has not proven possible to design artificial scattering structures that rival the performance of those found in organic structures. Contrarily, most sound manipulation relies on active transduction in fluid media rather than relying on passive scattering principles, as are often found in nature. In this work, we utilize computational morphogenesis to synthesize complex energy-efficient wavelength-sized single-material scattering structures that passively decompose radiated sound into its spatio-spectral components. Specifically, we tailor an acoustic rainbow structure with "above unity" efficiency and an acoustic wavelength-splitter. Our work paves the way for a new frontier in sound-field engineering, with potential applications in transduction, bionics, energy harvesting, communications and sensing.

cs.SD↗

arXiv2024

Extremal Structures with Embedded Pre-Failure Indicators

Preemptive identification of potential failure under loading of engineering structures is a critical challenge. Our study presents an innovative approach to built-in pre-failure indicators within multiscale structural designs utilizing the design freedom of topology optimization. The indicators are engineered to visibly signal load conditions approaching the global critical buckling load. By showing non-critical local buckling when activated, the indicators provide early warning without compromising the overall structural integrity of the design. This proactive safety feature enhances design reliability. With multiscale analysis, macroscale stresses are related to microscale buckling stability. This relationship is applied through tailored stress constraints to prevent local buckling in general while deliberately triggering it at predefined locations under specific load conditions. Experimental testing of 3D-printed designs confirms a strong correlation with numerical simulations. This not only demonstrates the feasibility of creating structures that can signal the need for load reduction or maintenance but also significantly narrows the gap between theoretical optimization models and their practical application. This research contributes to the design of safer structures by introducing built-in early-warning failure systems.

cs.CE↗

arXiv2024

Neural Networks for Generating Better Local Optima in Topology Optimization

Neural networks have recently been employed as material discretizations within adjoint optimization frameworks for inverse problems and topology optimization. While advantageous regularization effects and better optima have been found for some inverse problems, the benefit for topology optimization has been limited -- where the focus of investigations has been the compliance problem. We demonstrate how neural network material discretizations can, under certain conditions, find better local optima in more challenging optimization problems, where we here specifically consider acoustic topology optimization. The chances of identifying a better optimum can significantly be improved by running multiple partial optimizations with different neural network initializations. Furthermore, we show that the neural network material discretization's advantage comes from the interplay with the Adam optimizer and emphasize its current limitations when competing with constrained and higher-order optimization techniques. At the moment, this discretization has only been shown to be beneficial for unconstrained first-order optimization.

cs.LG↗

arXiv2024

Orders-of-magnitude reduction in photonic mode volume by nano-sculpting

Achieving strong light-matter interaction is important for studying and exploiting several physics phenomena. The light-matter interaction strength depends on the optical field intensity in the interaction region, often measured by the Purcell factor, which for a single emitter is proportional to the spectral confinement, quantified by the cavity quality factor $Q$, and inversely proportional to the spatial localization of light, quantified by the optical model volume $V$, $F \propto \frac{Q}{V}$. While plasmonic (metallic) devices can support extreme spatial light confinement, ohmic losses reduce the cavity lifetime, thereby limiting the achievable spectral confinement. It is therefore of both practical and fundamental interest to explore the potential for achieving extreme spatial light confinement in (near) loss-less dielectric environments. Employing topology optimization we explore the limits of spatial light confinement in dielectric environments when allowing for three-dimensional sculpted dielectric nanostructures. Here we discover structures supporting optical modes that are concentrated in material (air) with mode volumes that are three (four) orders of magnitude below the so-called diffraction limit, $V_{\textbf{r}_0} \approx 4 \cdot 10^{-4} \left[\lambda/(2 n)\right]^3 \left( V_{\textbf{r}_0} \approx 3 \cdot 10^{-5} \left[\lambda/2\right]^3\right)$. Remarkably, we further discover that encapsulating the nanostructure by ellipsoidal shells enables seemingly unbounded enhancement of the mode quality factor ($Q > 10^8$ demonstrated numerically) leading to theoretical Purcell factor enhancement above $10^{11}$. It is established how $V_{\textbf{r}_0}$ and $Q$ depend on the choice of material platform, device volume, minimum feature size and the number of shells. Finally a study of sensitivity towards geometric variations is presented, revealing robust behaviour.

physics.optics↗

arXiv2024

Topology optimization of contact-aided thermo-mechanical regulators

Topology optimization is used to systematically design contact-aided thermo-mechanical regulators, i.e. components whose effective thermal conductivity is tunable by mechanical deformation and contact. The thermo-mechanical interactions are modeled using a fully coupled non-linear thermo-mechanical finite element framework. To obtain the intricate heat transfer response, the components leverage self-contact, which is modeled using a third medium contact method. The effective heat transfer properties of the regulators are tuned by solving a topology optimization problem using a traditional gradient based algorithm. Several designs of thermo-mechanical regulators in the form of switches, diodes and triodes are presented.

cs.CE↗

arXiv2024

An 808 Line Phasor-Based Dehomogenisation Matlab Code For Multi-Scale Topology Optimisation

This work presents an 808-line Matlab educational code for combined multi-scale topology optimisation and phasor-based dehomogenisation titled deHomTop808. The multi-scale formulation utilises homogenisation of optimal microstructures to facilitate efficient coarse-scale optimisation. Dehomogenisation allows for a high-resolution single-scale reconstruction of the optimised multi-scale structure, achieving minor losses in structural performance, at a fraction of the computational cost, compared to its large-scale topology optimisation counterpart. The presented code utilises stiffness optimal Rank-2 microstructures to minimise the compliance of a single-load case problem, subject to a volume fraction constraint. By exploiting the inherent efficiency benefits of the phasor-based dehomogenisation procedure, on-the-fly dehomogenisation to a single-scale structure is obtained. The presented code includes procedures for structural verification of the final dehomogenised structure by comparison to the multi-scale solution. The code is introduced in terms of the underlying theory and its major components, including examples and potential extensions, and can be downloaded from https://github.com/peterdorffler/deHomTop808.git.

cs.MS↗