The paper introduces a cooperative neural network framework for partially inverse designing high‑performance concrete (HPC) mixes. It combines an imputation model with a surrogate strength predictor and is trained cooperatively, enabling it to produce valid, performance‑consistent mix designs in a single forward pass without retraining for different constraints. Compared to baseline methods, the approach achieves higher strength consistency (R² 0.84–0.89) and reduces mean squared error by 42–60%.
arXiv:2602. 00072v2 Announce Type: replace Abstract: The performance of machine learning surrogates is critically dependent on data quality and quantity.
arXiv:2606. 16032v1 Announce Type: cross Abstract: Interest in applying data-driven approaches in manufacturing has grown significantly, particularly for mapping complex, high-dimensional relationships.
The paper introduces LatentDDM, a neural operator framework that first pretrains on small subdomains and then adapts to new settings by training only a lightweight composition module. Experiments on steady Darcy flow and unsteady airfoil flow show that this approach reduces error by 36‑56% on larger domains and improves 20‑step rollouts, outperforming capacity‑matched full‑domain models. The study highlights co‑designed local pretraining and composition‑level transfer as a promising design principle for physical foundation models.
arXiv:2606. 04033v1 Announce Type: new Abstract: The validation of advanced nuclear reactor designs and fuel concepts requires critical experiments with high neutronic similarity to the target technology.
arXiv:2607. 09763v1 Announce Type: cross Abstract: Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability.
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.
The paper introduces a physics-informed neural network (PINN) framework for modeling fluid flow in dual‑network porous media, specifically addressing double porosity/permeability (DPP) systems. The framework embeds governing equations and boundary conditions into the loss function with adaptive weighting, employs dynamic collocation point selection, and uses shared trunk architectures to efficiently capture coupled pore‑network behavior. It is mesh‑free, accurately handles discontinuities across layered domains, and supports robust inverse analysis for parameter identification, with a systematic convergence study validating its stability and accuracy.
arXiv:2606. 06861v1 Announce Type: cross Abstract: Understanding nonlinear feature interactions is crucial in science and engineering, yet standard multilayer perceptrons (MLPs) often capture such interactions only implicitly, leading to entangled representations that can impair robustness and interpretability.
arXiv:2603. 15925v2 Announce Type: replace Abstract: Inverse design aims to find design parameters $x$ achieving target performance $y^*$.
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.
arXiv:2602. 22188v2 Announce Type: replace Abstract: Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces.
arXiv:2602. 15648v2 Announce Type: replace Abstract: Inverse design problems are common in engineering and materials science.