FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Data

• URL: http://arxiv.org/abs/2601.10031v1
• Date: Thu, 15 Jan 2026 03:22:03 GMT
• Title: FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Data
• Abstract summary: FilDeep is a Fidelity-based Deep Learning framework for large deformations in elastic-plastic solids.<n>We provide meticulous designs for the practical large deformation problem.<n>Our FilDeep consistently achieves state-of-the-art performance and can be efficiently deployed in manufacturing.
• Score: 26.050377173139694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising alternative. The effectiveness of current DL techniques typically depends on the availability of high-quantity and high-accuracy datasets, which are yet difficult to obtain in large deformation problems. During the dataset construction process, a dilemma stands between data quantity and data accuracy, leading to suboptimal performance in the DL models. To address this challenge, we focus on a representative application of large deformations, the stretch bending problem, and propose FilDeep, a Fidelity-based Deep Learning framework for large Deformation of elastic-plastic solids. Our FilDeep aims to resolve the quantity-accuracy dilemma by simultaneously training with both low-fidelity and high-fidelity data, where the former provides greater quantity but lower accuracy, while the latter offers higher accuracy but in less quantity. In FilDeep, we provide meticulous designs for the practical large deformation problem. Particularly, we propose attention-enabled cross-fidelity modules to effectively capture long-range physical interactions across MF data. To the best of our knowledge, our FilDeep presents the first DL framework for large deformation problems using MF data. Extensive experiments demonstrate that our FilDeep consistently achieves state-of-the-art performance and can be efficiently deployed in manufacturing.

Related papers

• SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility [71.76390626651254]
  We propose SPARD, a framework that establishes an automated, self-paced curriculum by perceiving learning progress to adjust dynamically multi-objective reward weights and data importance.<n>Extensive experiments across multiple benchmarks demonstrate that SPARD significantly enhances model capabilities across all domains.
  arXiv  Detail & Related papers  (2026-04-09T05:37:22Z)
• 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)
• Shifting AI Efficiency From Model-Centric to Data-Centric Compression [67.45087283924732]
  We argue that the focus of research for AI is shifting from model-centric compression to data-centric compression.<n>Data-centric compression improves AI efficiency by directly compressing the volume of data processed during model training or inference.<n>Our work aims to provide a novel perspective on AI efficiency, synthesize existing efforts, and catalyze innovation to address the challenges posed by ever-increasing context lengths.
  arXiv  Detail & Related papers  (2025-05-25T13:51:17Z)
• Paving the way for scientific foundation models: enhancing generalization and robustness in PDEs with constraint-aware pre-training [49.8035317670223]
  A scientific foundation model (SciFM) is emerging as a promising tool for learning transferable representations across diverse domains.<n>We propose incorporating PDE residuals into pre-training either as the sole learning signal or in combination with data loss to compensate for limited or infeasible training data.<n>Our results show that pre-training with PDE constraints significantly enhances generalization, outperforming models trained solely on solution data.
  arXiv  Detail & Related papers  (2025-03-24T19:12:39Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework [1.220743263007369]
  We propose a surrogate constraint (filtered PDE, FPDE in short) of the original physical equations to reduce the influence of noisy and sparse observation data. In the noise and sparsity experiment, the proposed FPDE models have better robustness than the conventional PDE models. For combining real-world experiment data into physics-informed training, the proposed FPDE constraint is useful.
  arXiv  Detail & Related papers  (2023-11-07T07:38:23Z)
• Machine Learning Force Fields with Data Cost Aware Training [94.78998399180519]
  Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation. Even for the most data-efficient MLFFs, reaching chemical accuracy can require hundreds of frames of force and energy labels. We propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
  arXiv  Detail & Related papers  (2023-06-05T04:34:54Z)
• To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
  Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs. We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting. Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
  arXiv  Detail & Related papers  (2023-05-22T17:02:15Z)
• Physics-aware deep learning framework for linear elasticity [0.0]
  The paper presents an efficient and robust data-driven deep learning (DL) computational framework for linear continuum elasticity problems. For an accurate representation of the field variables, a multi-objective loss function is proposed. Several benchmark problems including the Airimaty solution to elasticity and the Kirchhoff-Love plate problem are solved.
  arXiv  Detail & Related papers  (2023-02-19T20:33:32Z)
• Using Gradient to Boost the Generalization Performance of Deep Learning Models for Fluid Dynamics [0.0]
  We present a novel work to increase the generalization capabilities of Deep Learning. Our strategy has shown good results towards a better generalization of DL networks.
  arXiv  Detail & Related papers  (2022-10-09T10:20:09Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• A physics-informed variational DeepONet for predicting the crack path in brittle materials [3.1196544696082613]
  We propose a physics-informed variational formulation of DeepONet (V-DeepONet) for brittle fracture analysis. V-DeepONet is trained to map the initial configuration of the defect to the relevant fields of interests. We demonstrate the effectiveness of V-DeepOnet through two benchmarks of brittle fracture, and we verify its accuracy using results from high-fidelity solvers.
  arXiv  Detail & Related papers  (2021-08-16T05:31:05Z)

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.