Topology Optimization in Engineering

Derniers papiers

• Neural topology optimization of ship structures under propulsion machinery vibrations

  Shengyu Yan, Muhammad Muztahidul Hakim Zareer, Jasmin Jelovica · 30 septembre 2026

  Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological designs near resonance. This study extends neural-reparameterized topology optimization using a convolutional Kolmogorov-Arnold network (KATO) to for…

• Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design

  Mahish K. Guru, Mayank Nagar, Ayush vyas, Jan Bohlen, Roland Aydin, Noomane Ben Khalifa · 30 septembre 2026

  Inverse design of physical systems (molecules, devices, microstructures) often reduces to optimizing a high-dimensional structure against an expensive black-box simulator. Direct search is difficult because the space is non-Euclidean, feasibility is hard to encode, and each evaluation is expensive. …

• ReGDiff: Guided Diffusion in Regulated Latent Space for Exploring Metamaterial Voxel Geometry

  Wangzhi Zhan, Jianpeng Chen, Dongqi Fu, Dawei Zhou · 29 septembre 2026

  Metamaterials are artificially engineered structures whose mechanical and physical behaviors are strongly shaped by geometry rather than composition. Voxel representation provides a unified format for metamaterial geometry generation, as it can express diverse classes such as truss, shell, and porou…

• D-JEPA: Design-Recoverable JEPA Representation with Swappable Physics Decoders

  Nitin Nagesh Kulkarni, Aashwin Anand Mishra, Yin Yu, Peter Lyu · 29 septembre 2026

  Joint-Embedding Predictive Architectures (JEPAs) provide a framework for learning compact representations without directly reconstructing high-dimensional observations. However, in parameterized physical systems, learned representations can entangle geometry with operating conditions and task-specif…

• TopoMamba: A Load-Support Relation-Guided Multi-Directional State-Space Model for Topology Optimization

  Bin Lou, Yuxuan Cheng, Huaizhi Zong, Junhui Zhang, Bing Xu · 29 septembre 2026

  Deep learning has emerged as an efficient alternative for predicting high-performance material distributions in topology optimization. Existing methods struggle to accurately capture load-transfer information, limiting out-of-distribution generalization, while their model architectures often incur h…

• Growth-Inspired Graph Generation and Inverse Design of Mechanical Lattices via Dot Matrices Database Augmentation and GCNN

  Weiyun Xu, Jiamu Liu · 25 septembre 2026

  Natural load-bearing and transport networks are not assembled in a single step; they emerge through a temporally ordered process of growth, branching, reinforcement, and loop formation. Inspired by this developmental logic, this work introduces a morphogenetic graph-generation framework for mechanic…

• KATOsuper: Surrogate-accelerated neural topology optimization with sensitivity-consistent Fourier neural operators

  Shengyu Yan, Jasmin Jelovica · 24 septembre 2026

  Topology optimization (TO) remains computationally intensive due to repeated finite element analysis (FEA) evaluations required at each iteration. While neural network-based surrogates offer potential acceleration, existing approaches often suffer from gradient inconsistency between predicted object…

• GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model

  Trishit Mondal, Prajwal Bharadwaj, Nikhil Karanjgaokar, Ameya D. Jagtap · 22 septembre 2026

  Internal voids are ubiquitous defects in manufactured structures, yet their characterization remains challenging because their geometry is hidden and can only be inferred indirectly from accessible measurements. Here we introduce \textit{GenVoid}, a physics-informed generative model-based framework …

• Bilevel Optimization of Topology and Hyperparameters (BOTH)

  Suryanarayanan Manoj Sanu, Miguel Anibal Bessa, Alejandro Marcos Arag\'on · 21 septembre 2026

  Topology optimization (TO) represents a significant step towards automating the design process: given a working simulation, TO can produce a viable prototype at the press of a button by differentiating the simulation and iteratively improving the design. In practice, however, TO is riddled with ``ma…

• Zero-shot rib design: merging training-free generative prior with topology optimization

  Yongmin Kwon, Namwoo Kang · 11 septembre 2026

  Natural load-bearing patterns such as leaf venation, trabecular bone, and spider webs achieve high stiffness per unit mass, yet classical topology optimizers rarely reach such geometries, and few let engineers express structural design intent through natural language. This work treats a frozen text-…

• Conditional Flow Matching for ML-Based Inverse Design Problems

  Juliana Felder, Milad Habibi, Soheyl Massoudi, Mark Fuge · 2 septembre 2026

  Engineering inverse design is often limited by the high computational cost of iterative solvers for optimization problems constrained by partial differential equations (PDEs) and by their sensitivity to initialization. Deep generative models can produce candidate designs without rerunning the simula…

• TO-Agents: A Multi-Agent AI Framework for Subjective Preference-Guided Topology Optimization

  Isabella A. Stewart, Hongrui Chen, Faez Ahmed · 25 août 2026

  Topology optimization can generate efficient structures, but designers often must manually translate qualitative intent, such as desired visual style, product experience, or manufacturability into solver settings that are not directly tied to those preferences. We present TO-Agents, a multi-agent AI…

• On the Importance of Geometric Nonlinearity and Temperature-Dependent Properties in Multi-Material Thermo-Mechanical Topology Optimization

  Shirin Hosseinmardi, Xiangyu Sun, Ramin Bostanabad · 12 août 2026

  Thermo-mechanical compliant devices are commonly designed with small-strain linear elasticity and temperature-independent material properties, even though they might operate hundreds of kelvin above ambient where both assumptions are questionable. In this work, we quantify the effect and cost of eac…

• Surrogate Modeling for the Design of Optimal Lattice Structures using Tensor Completion

  Shaan Pakala, Aldair E. Gongora, Brian Giera, Evangelos E. Papalexakis · 4 août 2026

  When designing new materials, it is often necessary to design a material with specific desired properties. Unfortunately, as new design variables are added, the search space grows exponentially, which makes synthesizing and validating the properties of each material very impractical and time-consumi…

• Steering topology distributions for unified generative design of architected metamaterials

  Haolin Li, Yuyang Miao, Menglei Li, Jinshuai Bai, Liyuan Wang, Xin Liu, Bo Gao, Jiantao Liu, Danilo Mandic, Zahra Sharif Khodaei, M. H. Aliabadi, Weiqiu Chen · 29 juillet 2026

  Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design. However, existing design methods are often tailored to individual design problems, making limited use of topology knowledge for effective and broadly ap…

• Trajectory-Aware Flow Matching for Topology Optimisation

  Shusheng Xiao, Jinshuai Bai, Hyogu Jeong, Yunfei Xi, Yilin Gui, YuanTong Gu · 17 juillet 2026

  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. Generative TO offers a route to rapid design exploration, but existing model…

• A Learning-Based Ansatz Satisfying Boundary Conditions in Variational Problems

  Rafael Florencio, Julio Guerrero · 14 juillet 2026

  Recently, innovative adaptations of the Ritz method incorporating deep learning have been developed, known as the Deep Ritz Method. This approach employs a neural network as the trial function for variational problems. However, the neural network does not inherently satisfy the boundary conditions o…

• Incremental Transformer for Surrogate-Based Inverse Design of Geopolymer Mixtures

  Giansalvo Cirrincione, Filippo Grassia · 14 juillet 2026

  Small-data inverse design is challenging in engineering informatics when observations are heterogeneous, mixed-type, and constrained by physical relations among design variables. This work proposes a topology-aware surrogate framework guided by an Incremental Transformer (INCRT) for physics-constrai…

• Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization

  Xiangming Huang, Guannan Zhang, Lu Lu, Rapha\"el Pestourie · 9 juillet 2026

  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…

• HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization

  Jinbo Yang, Mingyue Yuan, Boyuan Zhang, Yoshifumi Kitamura, Shikai Jing · 9 juillet 2026

  Deep generative models offer a promising paradigm for topology optimization, enabling rapid design exploration. However, these approaches lack intrinsic physics guidance, often leading to poor generalizability across unseen boundary conditions and the formation of floating material artifacts. To add…

• CertMix: Certified, Data-Efficient Metamaterial Design by Affine Mixing of Aligned Neural-Implicit Weight Spaces

  Yifan Wang · 7 juillet 2026

  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. We introduce …

• eCNNTO: A Highly Generalizable ConvNet for Accelerating Topology Optimization

  Shengbiao Lu, Xiaodong Wei · 19 juin 2026

  This work proposes an element-based Convolutional Neural Network (CNN) to accelerate density-based Topology Optimization (TO), termed eCNNTO. TO generally undergoes a large number of iterations, where finite element analysis is performed in every iteration, leading to the efficiency bottleneck espec…

• A Multi-Agent System for IPMSM Design Optimization via an FEA-AI Hybrid Approach

  Jinseong Han, Sunwoong Yang, Namwoo Kang · 9 juin 2026

  Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in spar…

• Agentic Large Language Models for Automated Structural Analysis of 3D Frame Systems

  Ziheng Geng, Ian Franklin, Santiago Martinez, Jiachen Liu, Yunhe Zhao, Minghui Cheng · 8 juin 2026

  Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains. Beyond reactive text generation, agentic LLMs enable autonomous workflow execution through modular task decomposition and coordinated tool use. In structural engineering, recent…

• On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching

  Mohammad Rashed, Duarte F. Valoroso Madeira, Babak Gholami, Caglar Guerbuez, Yunjia Yang, Nils Thuerey · 2 juin 2026

  Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as changing loads or boundary conditions, yet the source of this variability remains unclear. We hypothesize that OOD performance is governed by how much in…