The paper introduces Decomposition-Aware Distributional Optimization (DADO), a new algorithm that exploits decomposability in property predictors to improve in‑silico design of discrete objects such as proteins, circuits, and materials. DADO uses a soft‑factorized search distribution and graph message‑passing to coordinate optimization across linked factors defined by a junction tree over design variables. The method aims to make distributional optimization over combinatorial design spaces more efficient by leveraging the structure of the predictive model.
By James C. Bowden, Sergey Levine, Jennifer Listgarten
The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often lack robustness and transferability, whereas evolutionary strategies are robust but struggle in high-dimensional spaces.
arXiv:2607. 13688v1 Announce Type: new Abstract: Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior.
By J. Storm, I. B. C. M. Rocha, S. Schyck, K. Masania, F. P. van der Meer
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...
By Xiang Chen, Huanhuan Xia
arXiv:2607. 24274v1 Announce Type: cross Abstract: Inverse design of three-dimensional porous media is central to applications in filtration, catalysis, energy storage, fuel cells, thermal management, and biomedical scaffolds, but remains challenging because many distinct pore geometries can share similar porosity or permeability while small structural changes can strongly affect transport behaviour.
By Peng Wang
arXiv:2607. 07682v1 Announce Type: new Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces.
By Xiangming Huang, Guannan Zhang, Lu Lu, Rapha\"el Pestourie
arXiv:2510. 07474v2 Announce Type: replace Abstract: When designing new materials, it is often necessary to design a material with specific desired properties.
By Shaan Pakala, Aldair E. Gongora, Brian Giera, Evangelos E. Papalexakis
arXiv:2602. 10392v2 Announce Type: replace Abstract: When designing new materials, it is often necessary to tailor the material design to have some desired properties.
By Shaan Pakala, Aldair E. Gongora, Brian Giera, Evangelos E. Papalexakis
The paper introduces Conditional Flow Matching (CFM) for engineering inverse design, comparing it to conditional diffusion models and cGANs on EngiBench structural and thermal benchmarks. CFM outperforms the baselines in cumulative and final optimality gaps, mean volume‑fraction deviation, and throughput, achieving up to 66× faster sample generation with fewer network evaluations. The study demonstrates CFM’s effectiveness as a warm‑start generator for gradient‑based refinement in PDE‑constrained design problems.
By Juliana Felder, Milad Habibi, Soheyl Massoudi, Mark Fuge
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.
By Yang Hu, Na Li
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.
By Kijung Jeon, Michael Muehlebach, Molei Tao
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.
By Giansalvo Cirrincione, Filippo Grassia