The paper introduces Differentiable Diffusion Conditioning (D2C), an offline model‑based optimization approach that turns a frozen conditional diffusion model into a gradient‑based optimizer. By making the reverse denoising chain differentiable with respect to conditioning signals, D2C optimizes conditioning variables using gradients from surrogate objectives and physics‑informed penalties. The method is evaluated on laser pulse optimization for inertial confinement fusion, sustainable data‑center workload scheduling, and four Design‑Bench tasks, achieving top results on the real‑world tasks and the best average rank across the benchmarks.
arXiv:2604. 17838v2 Announce Type: replace Abstract: Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.
arXiv:2606. 04468v1 Announce Type: cross Abstract: Offline multi-objective optimization (Offline MOO) aims to discover novel Pareto-optimal designs based on static datasets without expensive environment interactions.
arXiv:2406.04261v2 Announce Type: replace Abstract: Simulators are vital in science and engineering, as they faithfully model the influence of design parameters on real-world observations. Optimizing...
The paper introduces D$^3$Opt, a derivative‑free diffusion‑based framework that separates constraint modeling from objective optimization in chance‑constrained programs. It learns a risk‑conditioned diffusion model once on constraint‑filtered decisions, then uses an annealed, particle‑based Feynman–Kac correction to optimize any post‑specified objective without retraining. The method is proven to preserve feasibility and provides an error bound, and experiments show effective optimization across smooth, non‑smooth, and non‑convex objectives, including objective transfer tasks.
arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.
arXiv:2602.12624v2 Announce Type: replace Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by h...
arXiv:2609. 22752v1 Announce Type: new Abstract: Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samples.
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 explores how to use gradients of simulator outputs with respect to input parameters to improve Bayesian inference for expensive simulators. By incorporating gradient information into Gaussian process surrogate models, the authors show that Bayesian optimization can converge faster under a limited simulation budget. They find that reverse‑mode differentiation yields efficiency gains, while forward‑mode does not offset its computational cost, especially when the number of parameters exceeds the output dimensionality.
arXiv:2608. 16080v1 Announce Type: new Abstract: Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve.
Score‑based diffusion models excel at inverse problems but suffer from slow inference and task‑specific hyperparameter tuning. This paper introduces principled reparameterizations that create invariances, enabling the reuse of the same hyperparameters across different tasks. Building on the RED‑diff framework, the authors present OptDiff, a streamlined pipeline that integrates convex optimization to accelerate inference, achieving notable speedups and higher image quality in reconstruction, deblurring, and super‑resolution experiments.
arXiv:2602. 08210v2 Announce Type: replace Abstract: Heatmap-based solvers have emerged as a promising paradigm for Combinatorial Optimization (CO).