The paper introduces the ICF Laser Pulse Shape Design System (LPDS), a generative inverse modeling framework that directly maps desired fusion outcomes and pellet configurations to optimized laser pulse shapes. It incorporates a multi-objective loss function to enforce physical constraints and experimental requirements, and offers tools such as constraint-conditioning, inpainting, and gradient-based editing for fine-grained pulse control. The authors also demonstrate the framework’s applicability to a limited set of real experimental shots, positioning LPDS as a data-driven approach to advance practical inertial confinement fusion.
arXiv:2607. 10366v1 Announce Type: new Abstract: Offline-trained surrogates for Inertial Confinement Fusion (ICF) suffer a well-known failure mode that iterative optimizers drive inputs into out-of-distribution (OOD) regions where predictions become unreliable.
arXiv:2609.13819v1 Announce Type: new Abstract: Solving inverse problems with differentiable physics simulators holds the potential to revolutionize scientific discovery and engineering design, as it...
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:2602. 15648v2 Announce Type: replace Abstract: Inverse design problems are common in engineering and materials science.
arXiv:2607. 10896v1 Announce Type: new Abstract: Small-data inverse design is challenging in engineering informatics when observations are heterogeneous, mixed-type, and constrained by physical relations among design variables.
arXiv:2509. 05510v3 Announce Type: replace-cross Abstract: Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input parameters, followed by design optimization.
arXiv:2508. 09156v3 Announce Type: replace-cross Abstract: We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems.
arXiv:2603. 15925v2 Announce Type: replace Abstract: Inverse design aims to find design parameters $x$ achieving target performance $y^*$.
Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one million dollars, making accurate AI surrogates a h...
PMosFM introduces a preconditioned manifold matching framework that enables one‑step physics‑constrained generation by encoding constraints in a manifold decoder. The method learns transport in intrinsic coordinates, eliminating the need for residual losses or trajectory unrolling, and employs a geometric preconditioner and covariance transform to improve conditioning. Experiments demonstrate reduced training and sampling time compared to multi‑step baselines while maintaining comparable physical and distributional fidelity.
arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.
The paper introduces a method for Bayesian optimisation in engineering design problems where both the objective and constraints depend on the state of a PDE solution. It proposes pre‑computing a set of controls whose linearised constraint response remains within tolerance, effectively pulling the state‑space constraint into design space and forming an ellipsoid from which well‑spread candidates can be drawn. The linear response map is refined online, and the approach is demonstrated on Tokamak divertor optimisation with plasma‑boundary preservation.