arXiv subjects

Cedric Caremel

Publications and source records attributed to Cedric Caremel.

5 recordsLinked to original sources

arXiv2026

TopoStyle: Supporting Iterative Design with Generative AI for 2.5D Topology Optimization

Topology optimization(TO) is widely used in engineering because of its ability to save material and optimize structural performance. Although prior work has explored 2D human-centered design tool for TO, the results are often limited in variety and offer weak customizability. Meanwhile, due to the high computational and time costs of TO, researchers have attempted to address these issues using generative AI; however, such methods often provide limited interactivity. In addition, topology optimization in many cases needs to balance structural performance and aesthetic qualities through iterative design, a perspective that has rarely been emphasized in traditional TO. We present TopoStyle, an iterative design tool for 2.5D topology optimization using a 2D diffusion model. We explore two interaction methods. The first exports 3D parts to a graphical interface for hand-drawn interaction. The second enables direct interaction within 3D modeling software using points. Our tool also supports the use of masks to apply topology optimization to specific regions, allowing users to address customized design needs. We compare and evaluate both performance and interaction methods, and investigate how TopoStyle can balance performance and aesthetics while improving design efficiency through customization and iterative design. Finally, we demonstrate the application scenarios of TopoStyle through several design cases.

cs.HC↗

arXiv2026

Sketch2Topo: Using Hand-Drawn Inputs for Diffusion-Based Topology Optimization

Topology optimization (TO) is employed in engineering to optimize structural performance while maximizing material efficiency. However, traditional TO methods incur significant computational and time costs. Although research has leveraged generative AI to predict TO outcomes and validated feasibility and accuracy, existing approaches still suffer from limited customizability and impose a high cognitive load on users. Furthermore, balancing structural performance with aesthetic attributes remains a persistent challenge. We developed Sketch2Topo, which augments a diffusion-based TO model with image-to-image generation and image editing capabilities. With Sketch2Topo, users can use sketching to customize geometries and specify physical constraints. The tool also supports mask input, enabling users to perform TO on selected regions only, thereby supporting higher levels of customization. We summarize the workflow and details of the tool and conduct a brief quantitative evaluation. Finally, we explore application scenarios and discuss how hand-drawn input improves usability while balancing functionality and aesthetics.

cs.HC↗

arXiv2025

Magnetic field estimation using Gaussian process regression for interactive wireless power system design

Wireless power transfer (WPT) with coupled resonators offers a promising solution for the seamless powering of electronic devices. Interactive design approaches that visualize the magnetic field and power transfer efficiency based on system geometry adjustments can facilitate the understanding and exploration of the behavior of these systems for dynamic applications. However, typical electromagnetic field simulation methods, such as the Method of Moments (MoM), require significant computational resources, limiting the rate at which computation can be performed for acceptable interactivity. Furthermore, the system's sensitivity to positional and geometrical changes necessitates a large number of simulations, and structures such as ferromagnetic shields further complicate these simulations. Here, we introduce a machine learning approach using Gaussian Process Regression (GPR), demonstrating for the first time the rapid estimation of the entire magnetic field and power transfer efficiency for near-field coupled systems. To achieve quick and accurate estimation, we develop 3D adaptive grid systems and an active learning strategy to effectively capture the nonlinear interactions between complex system geometries and magnetic fields. By training a regression model, our approach achieves magnetic field computation with sub-second latency and with an average error of less than 6% when validated against independent electromagnetic simulation results.

physics.app-ph↗

arXiv2025

Suppressing Leakage Magnetic Field in Wireless Power Transfer using Halbach Array-Based Resonators

Wireless power transfer has the potential to seamlessly power electronic systems, such as electric vehicles, industrial robots, and mobile devices. However, the leakage magnetic field is a critical bottleneck that limits the transferable power level, and heavy ferromagnetic shields are needed for transferring large amounts of power. In this paper, we propose a ferrite-less coil design that generates an asymmetric magnetic field pattern focused on one side of the resonator, which effectively reduces the leakage magnetic field. The key to enabling the asymmetric field pattern is a coil winding strategy inspired by the Halbach array, a permanent magnet arrangement, which is then tailored for wireless power using an evolutionary strategy algorithm. Numerical analyses and simulations demonstrated that the proposed coil structure delivers the same amount of power as spiral coils, while achieving an 86.6% reduction in magnetic field intensity at a plane located 75 mm away from the resonator pair and a power efficiency of 96.0%. We verified our approach by measuring the power efficiency and magnetic field intensity of a test wireless power system operating at 6.78 MHz. These findings indicate that our approach can efficiently deliver over 50 times more power without increasing magnetic field exposure, making it a promising solution for high-power wireless power transfer applications.

physics.app-ph↗

arXiv2024

Plug-n-play e-knit: prototyping large-area e-textiles using machine-knitted magnetically-repositionable sensor networks

Prototyping electronic textile (e-textile) involves embedding electronic components into fabrics to develop smart clothing with specific functionalities. However, this process is still challenging since the complicated wiring setup is required during experimental phases. This paper presents plug-n-play e-knit, a large-scale, repositionable e-textile for providing trial-and-error prototyping platforms across the textile. Plug-n-play e-knit leverages industrial digital knitting machines loaded with conductive thread to automatically embed a communication and power supply network into garments, in addition to using soft magnet connectors to rearrange electronic components while preserving the stretchability of the garment. These combinations enable users to quickly establish e-textile sensor networks, and moreover test the performance and optimal placement of the electric devices on the textile. We demonstrated that our textiles leveraging custom I2C protocols could achieve the motion-resilient motion-tracking sensor network over a 2700 $cm^2$ garment area.

cs.HC↗