Ivan Kalthoff | Searcharxiv

arXiv2026

Light-propelled microparticles based on symmetry-broken refractive index profiles

Active colloidal microparticles require reliable actuation to sustain directed motion. Light-based propulsion is particularly attractive as it provides persistent energy supply and enables direct spatiotemporal control. Here, we introduce 3D-printable particles with symmetry-broken refractive index profiles (SBRIP particles) that achieve propulsion through direct momentum transfer from asymmetric light refraction. Internal refractive-index gradients provide optical symmetry breaking independent of external shape, fundamentally decoupling propulsion from particle geometry. Geometrically symmetry-broken particles with a homogeneous refractive index are another special case, where propulsion originates from refractive contrast at the boundary instead of within the particle. Unlike conventional systems relying on absorption or reflection, this transparency-based mechanism minimizes heating and mitigates shadowing in bulk suspensions. We present a theoretical framework for refractive propulsion as well as numerical simulations of the SBRIP particles using raytracing and the finite volume method. This is complemented by experiments, validating the momentum transfer mechanism using particles with geometric symmetry breaking. The high transparency of our particles ensures deep light penetration, enabling the realization of volumetric active matter. This opens pathways toward adaptive nonlinear optical materials where light-driven particle reorganization modulates the local refractive index, establishing a dynamic feedback loop between the optical field and the material structure.

physics.optics↗

arXiv2025

Acoustic neural networks: Identifying design principles and exploring physical feasibility

Wave-guide-based physical systems provide a promising route toward energy-efficient analog computing beyond traditional electronics. Within this landscape, acoustic neural networks represent a promising approach for achieving low-power computation in environments where electronics are inefficient or limited, yet their systematic design has remained largely unexplored. Here we introduce a framework for designing and simulating acoustic neural networks, which perform computation through the propagation of sound waves. Using a digital-twin approach, we train conventional neural network architectures under physically motivated constraints including non-negative signals and weights, the absence of bias terms, and nonlinearities compatible with intensity-based, non-negative acoustic signals. Our work provides a general framework for acoustic neural networks that connects learnable network components directly to physically measurable acoustic properties, enabling the systematic design of realizable acoustic computing systems. We demonstrate that constrained recurrent and hierarchical architectures can perform accurate speech classification, and we propose the SincHSRNN, a hybrid model that combines learnable acoustic bandpass filters with hierarchical temporal processing. The SincHSRNN achieves up to 95% accuracy on the AudioMNIST dataset while remaining compatible with passive acoustic components. Beyond computational performance, the learned parameters correspond to measurable material and geometric properties such as attenuation and transmission. Our results establish general design principles for physically realizable acoustic neural networks and outline a pathway toward low-power, wave-based neural computing.

cs.SD↗