GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data

• URL: http://arxiv.org/abs/2506.12051v2
• Date: Sun, 24 Aug 2025 21:14:59 GMT
• Title: GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data
• Abstract summary: GUST (Generative Uncertainty learning via Self-supervised pretraining and Transfer learning) is a framework for quantifying free-form geometric uncertainties inherent in the manufacturing of metamaterials.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces GUST (Generative Uncertainty learning via Self-supervised pretraining and Transfer learning), a framework for quantifying free-form geometric uncertainties inherent in the manufacturing of metamaterials. GUST leverages the representational power of deep generative models to learn a high-dimensional conditional distribution of as-fabricated unit cell geometries given nominal designs, thereby enabling uncertainty quantification. To address the scarcity of real-world manufacturing data, GUST employs a two-stage learning process. First, it leverages self-supervised pretraining on a large-scale synthetic dataset to capture the structure variability inherent in metamaterial geometries and an approximated distribution of as-fabricated geometries given nominal designs. Subsequently, GUST employs transfer learning by fine-tuning the pretrained model on limited real-world manufacturing data, allowing it to adapt to specific manufacturing processes and nominal designs. With only 960 unit cells additively manufactured in only two passes, GUST can capture the variability in geometry and effective material properties. In contrast, directly training a generative model on the same amount of real-world data proves insufficient, as demonstrated through both qualitative and quantitative comparisons. This scalable and cost-effective approach significantly reduces data requirements while maintaining the effectiveness in learning complex, real-world geometric uncertainties, offering an affordable method for free-form geometric uncertainty quantification in the manufacturing of metamaterials. The capabilities of GUST hold significant promise for high-precision industries such as aerospace and biomedical engineering, where understanding and mitigating manufacturing uncertainties are critical.

Related papers

• Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning [48.04423567569116]
  We propose a sparsity-promoting fine-tuning method that selectively updates model parameters.<n>On energy and force prediction tasks, our method matches or surpasses full fine-tuning and equivariant low-rank adaptation.<n>Our results establish sparsity-promoting fine-tuning as a flexible and interpretable method for domain specialization of equivariant materials foundation models.
  arXiv  Detail & Related papers  (2026-06-17T05:10:18Z)
• CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts [0.3250512744763586]
  Graph-based machine learning has emerged as a promising approach for manufacturability analysis by learning directly from CAD models represented as Boundary Representations (B-reps)<n>We propose a hybrid approach that addresses this challenge by enriching B-rep attributed adjacency graphs with manufacturing features recognized through a rule-based module.<n> Experiments on both a large-scale synthetic manufacturability benchmark and a real-world industrial dataset with measured bending times, demonstrate that combining domain knowledge with graph-based learning improves prediction accuracy across both tasks.
  arXiv  Detail & Related papers  (2026-05-12T15:30:12Z)
• Scalable Learning of Multivariate Distributions via Coresets [48.02318786960811]
  We develop a novel coreset construction for multivariate conditional transformation models (MCTMs) to enhance their scalability and training efficiency.<n>Our approach yields substantial data reduction via importance sampling.<n>Compared to conventional full-parametric models, where coresets have been incorporated before, our semi-parametric approach exhibits enhanced adaptability.
  arXiv  Detail & Related papers  (2026-03-20T09:28:43Z)
• BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending [0.5156484100374058]
  We propose a taxonomy of manufacturability metrics along the axes of configuration dependence and measurement type.<n>We introduce BenDFM, the first synthetic dataset for manufacturability assessment in sheet metal bending.<n>BenDFM contains 20,000 parts, both manufacturable and unmanufacturable, generated with process-aware bending simulations.
  arXiv  Detail & Related papers  (2026-03-13T15:57:44Z)
• Equivariant Evidential Deep Learning for Interatomic Potentials [55.6997213490859]
  Uncertainty quantification is critical for assessing the reliability of machine learning interatomic potentials in molecular dynamics simulations.<n>Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance.<n>We propose textitEquivariant Evidential Deep Learning for Interatomic Potentials ($texte2$IP), a backbone-agnostic framework that models atomic forces and their uncertainty jointly.
  arXiv  Detail & Related papers  (2026-02-11T02:00:25Z)
• LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization [7.8639568562295965]
  We consider small-data, large-scale decision problems in which a firm must make many operational decisions simultaneously.<n>We propose a pretrain-then-finetune approach built on a designed Transformer model to address this challenge.
  arXiv  Detail & Related papers  (2026-02-03T16:08:33Z)
• Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets [0.32622301272834514]
  We assess the effectiveness of graph neural network (GNN) models trained on two fundamentally different types of datasets.<n>As a case study, we address the challenging task of predicting electronic and mechanical properties of a prototypical family of optoelectronic materials.<n>We find that the phonons-informed model consistently outperforms the randomly trained counterpart, despite relying on fewer data points.
  arXiv  Detail & Related papers  (2025-11-19T08:16:10Z)
• Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys [9.066691897904875]
  Machine learning has significantly advanced the understanding and application of structural materials.<n>This study presents a comprehensive methodology utilizing a mixed density network (MDN) model.<n>The incorporation of statistical parameters of those predicted distributions into a dislocation-mediated plasticity model allows for accurate stress-strain predictions.
  arXiv  Detail & Related papers  (2025-06-25T21:18:14Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids [21.697159152687288]
  We introduce LaDEEP, a deep learning-based surrogate model for textbfLarge textbfDeformation of textbfElastic-textbfPlastic Solids.<n>To characterize the physical process of the solid deformation, a two-stage Transformer-based module is designed to predict the deformation with the sequence of tokens as input.<n>LaDEEP achieves five magnitudes faster speed than finite element methods with a comparable accuracy, and gains 20.47% relative improvement on average compared to other deep learning baselines.
  arXiv  Detail & Related papers  (2025-06-06T11:47:37Z)
• Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing [0.0]
  Metal additive manufacturing enables unprecedented design freedom and the production of customized, complex components.<n>The rapid melting and solidification dynamics inherent to metal AM processes generate heterogeneous, non-equilibrium microstructures.<n>Predicting microstructure and its evolution across spatial and temporal scales remains a central challenge for process optimization and defect mitigation.
  arXiv  Detail & Related papers  (2025-05-02T17:59:54Z)
• Training-Free Constrained Generation With Stable Diffusion Models [45.138721047543214]
  Stable diffusion models represent the state-of-the-art in data synthesis across diverse domains.<n>This paper proposes a novel integration of stable diffusion models with constrained optimization frameworks.<n>The effectiveness of this approach is demonstrated through material design experiments requiring adherence to precise morphometric properties.
  arXiv  Detail & Related papers  (2025-02-08T16:11:17Z)
• Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction [0.0]
  We present a foundation model specifically designed for composite materials.<n>Our findings validate the feasibility and effectiveness of foundation models in composite materials.<n>This framework enables high-accuracy predictions even when experimental data are scarce.
  arXiv  Detail & Related papers  (2024-11-10T19:06:25Z)
• Fine-Tuned Language Models Generate Stable Inorganic Materials as Text [57.01994216693825]
  Fine-tuning large language models on text-encoded atomistic data is simple to implement yet reliable. We show that our strongest model can generate materials predicted to be metastable at about twice the rate of CDVAE. Because of text prompting's inherent flexibility, our models can simultaneously be used for unconditional generation of stable material.
  arXiv  Detail & Related papers  (2024-02-06T20:35:28Z)
• Generative Learning of Continuous Data by Tensor Networks [45.49160369119449]
  We introduce a new family of tensor network generative models for continuous data. We benchmark the performance of this model on several synthetic and real-world datasets. Our methods give important theoretical and empirical evidence of the efficacy of quantum-inspired methods for the rapidly growing field of generative learning.
  arXiv  Detail & Related papers  (2023-10-31T14:37:37Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Electronic Structure Prediction of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning [5.4875371069660925]
  Ground state electron density -- obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations -- contains a wealth of material information. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data.
  arXiv  Detail & Related papers  (2023-08-24T21:41:29Z)
• FAENet: Frame Averaging Equivariant GNN for Materials Modeling [123.19473575281357]
  We introduce a flexible framework relying on frameaveraging (SFA) to make any model E(3)-equivariant or invariant through data transformations. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling.
  arXiv  Detail & Related papers  (2023-04-28T21:48:31Z)
• Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System [2.530807828621263]
  The emergence of Internet of Things (IoT) and Machine Learning (ML) has made the concept of surrogate modeling even more viable. This chapter begins with a brief overview of the concept of surrogate modeling, transfer learning, IoT and digital twins. After that, a detailed overview of uncertainties, uncertainty quantification frameworks, and specifics of uncertainty quantification methodologies for a surrogate model linked to a digital twin is presented.
  arXiv  Detail & Related papers  (2022-09-30T20:19:04Z)
• Privacy-preserving machine learning with tensor networks [37.01494003138908]
  We show that tensor network architectures have especially prospective properties for privacy-preserving machine learning. First, we describe a new privacy vulnerability that is present in feedforward neural networks, illustrating it in synthetic and real-world datasets. We rigorously prove that such conditions are satisfied by tensor-network architectures.
  arXiv  Detail & Related papers  (2022-02-24T19:04:35Z)
• Intelligent multiscale simulation based on process-guided composite database [0.0]
  We present an integrated data-driven modeling framework based on process modeling, material homogenization, and machine learning. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries.
  arXiv  Detail & Related papers  (2020-03-20T20:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.