The paper introduces RL‑Kirigami, an inverse design framework that uses optimal‑transport conditional flow matching (OT‑CFM) and reinforcement learning to generate compatible ratio fields for compact reconfigurable parallelogram quad kirigami. A marching decoder enforces global geometric compatibility, while Group Relative Policy Optimization (GRPO) aligns the generator with nondifferentiable rewards for silhouette matching, feasibility, and ratio‑field regularity. The method achieves high silhouette IoU scores (up to 94.91%) and reduces forward simulator evaluations from hundreds to one, enabling rapid laser‑cut prototypes in polymeric sheets within about eight minutes.
arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.
arXiv:2607. 04123v1 Announce Type: new Abstract: Inverse design of mechanical metamaterials seeks a periodic unit cell whose homogenized elastic properties meet a prescribed target, but current learning-based methods are data-hungry, mostly interpolative, and provide no guarantee that the generated design satisfies the specification.
arXiv:2602. 15648v2 Announce Type: replace Abstract: Inverse design problems are common in engineering and materials science.
The paper introduces a reward‑directed diffusion framework for localized ship hull optimization, using parametric masking to focus changes on specific hull regions while maintaining overall continuity. Surrogate‑assisted sampling targets low calm‑water resistance, and a high‑fidelity CFD pipeline evaluates the designs, revealing trade‑offs between friction and pressure that are not captured by empirical screening. The study demonstrates that detailed assessment can uncover limitations in the generative model and guide better objective formulations for realistic hull designs.
arXiv:2607. 24777v1 Announce Type: new Abstract: Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design.
The paper introduces Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing (A‑RAM), a framework that transforms user intent and part files into execution‑ready plans for robotic AM. It uses a large language model to interpret manufacturing goals, a deterministic Planning Agent to generate search workflows, and domain tools to evaluate slicing, placement, inverse kinematics, trajectory timing, Joint‑6 jerk, and extrusion. Experiments on a six‑axis robotic‑arm AM cell show that A‑RAM can reduce maximum Joint‑6 jerk by up to 53.5 % and mean absolute Joint‑6 jerk by 48.3 %, while also shortening motion‑plan completion times and extrusion paths.
arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.
The paper introduces Dynamic Homing Optimization (DHO), a forward‑process reinforcement learning approach that reinterprets negative‑trajectory optimization as attraction toward positive samples. DHO uses Minimum‑Cost Attractive Matching to assign each negative sample a distinct positive target and Time‑Aware Dynamic Correction to guide trajectories toward those targets. Building on DHO, the authors present Flow3D‑Pro, an image‑to‑3D geometry generation framework that outperforms existing DPO, GRPO, and NFT‑style objectives and produces higher‑quality 3D meshes.
arXiv:2607. 14652v1 Announce Type: new Abstract: Topology optimisation (TO) often requires repeated finite element analysis and sensitivity-based material updates, which can be costly when multiple candidate designs are needed under varying physical and design conditions.
arXiv:2604. 01480v2 Announce Type: replace Abstract: Metasurface inverse design can realize complex optical functionality, but turning a target optical response into executable optimization code still requires substantial expertise in computational electromagnetics and solver-specific software engineering.
ArcFlow is a deployment‑aware algorithm that trains a single flow policy to perform well across a range of solver budgets. It combines action‑aligned supervision, budget exposure, and worst‑budget checkpoint selection, using a surrogate ratio and PPO‑style clip for reconstruction objectives and multi‑budget endpoint supervision for velocity objectives. Experiments on DM Control, manipulation, and locomotion tasks show ArcFlow achieves the highest mean one‑step return among five flow‑policy baselines on 25 of 30 tasks, demonstrating that training with multiple budgets yields strong low‑step control with a single policy.
arXiv:2607. 23469v1 Announce Type: cross Abstract: Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled parameters requires costly full-wave simulations.