:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Song, Siyuan, Jin, Hanxun
Format:	Preprint
Published:	2023
Subjects:	Materials Science Machine Learning
Online Access:	https://arxiv.org/abs/2308.15640
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
by: Garban, Manuel Ricardo Guevara, et al.
Published: (2025)

Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses
by: Jafarzadeh, Siavash, et al.
Published: (2024)

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling
by: Taj, Reyhaneh
Published: (2024)

Physics-Informed Gaussian Process Regression for the Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models
by: Li, Chenyang, et al.
Published: (2026)

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
by: Park, Eunjeong, et al.
Published: (2026)

Theory and implementation of inelastic Constitutive Artificial Neural Networks
by: Holthusen, Hagen, et al.
Published: (2023)

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
by: Li, Chaojian, et al.
Published: (2025)

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
by: Polat, Can, et al.
Published: (2025)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
by: Jia, Shuyi, et al.
Published: (2025)

Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements
by: Jafarzadeh, Siavash, et al.
Published: (2024)

Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy
by: Lee, Wei Shan, et al.
Published: (2025)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
by: Benítez, Pol, et al.
Published: (2025)

The Language of Hyperelastic Materials
by: Kissas, Georgios, et al.
Published: (2024)

Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)
by: Gray, Zoë J., et al.
Published: (2025)

Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
by: Robertson, Andreas E., et al.
Published: (2025)

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
by: Lee, Wei Shan, et al.
Published: (2025)

Clever Materials: When Models Identify Good Materials for the Wrong Reasons
by: Jablonka, Kevin Maik
Published: (2026)

Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
by: Petković, Marko, et al.
Published: (2025)

ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
by: Huang, Jiao, et al.
Published: (2024)

A General Neural Network Potential for Energetic Materials with C, H, N, and O elements
by: Wen, Mingjie, et al.
Published: (2025)

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
by: Schoener, Brandon, et al.
Published: (2026)

Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture
by: Mishra, Akshansh
Published: (2024)

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
by: Li, Jiaxiang, et al.
Published: (2025)

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials
by: Koker, Teddy, et al.
Published: (2023)

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024)

Thermodynamically-Informed Iterative Neural Operators for Heterogeneous Elastic Localization
by: Kelly, Conlain, et al.
Published: (2024)

Discovery of Hyperelastic Constitutive Laws from Experimental Data with EUCLID
by: Abbasi, Arefeh, et al.
Published: (2025)

Stress Predictions in Polycrystal Plasticity using Graph Neural Networks with Subgraph Training
by: Zhai, Hanfeng
Published: (2024)

Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution
by: Kakhandiki, Pranav, et al.
Published: (2025)

Space Group Informed Transformer for Crystalline Materials Generation
by: Cao, Zhendong, et al.
Published: (2024)

Training Variation of Physically-Informed Deep Learning Models
by: Lenau, Ashley, et al.
Published: (2025)

Generating Symmetric Materials using Latent Flow Matching
by: Karmush, Anmar, et al.
Published: (2026)

Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
by: Sun, Yixuan, et al.
Published: (2020)

Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
by: Li, Longze, et al.
Published: (2023)

Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
by: Hardcastle, Christofer, et al.
Published: (2025)

Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization for Complex Materials Design Campaigns
by: Alvi, Sk Md Ahnaf Akif, et al.
Published: (2025)

Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials
by: Xing, Tengfei, et al.
Published: (2025)

Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials
by: Hollenweger, Yannick, et al.
Published: (2025)

DMFlow: Disordered Materials Generation by Flow Matching
by: Wu, Liming, et al.
Published: (2026)

Learning Physics-Consistent Material Behavior from Dynamic Displacements
by: Han, Zhichao, et al.
Published: (2024)