Saved in:
| Main Author: | Taj, Reyhaneh |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.10483 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
by: Song, Siyuan, et al.
Published: (2023)
by: Song, Siyuan, et al.
Published: (2023)
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
by: Li, Chaojian, et al.
Published: (2025)
by: Li, Chaojian, et al.
Published: (2025)
Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred Potential
by: Mao, Zetian, et al.
Published: (2024)
by: Mao, Zetian, 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)
by: Gray, Zoë J., 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)
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)
by: Jia, Shuyi, et al.
Published: (2025)
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)
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)
by: Benítez, Pol, et al.
Published: (2025)
Training Variation of Physically-Informed Deep Learning Models
by: Lenau, Ashley, et al.
Published: (2025)
by: Lenau, Ashley, 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)
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)
by: Lee, Wei Shan, et al.
Published: (2025)
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
by: Petković, Marko, et al.
Published: (2025)
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)
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)
by: Wen, Mingjie, et al.
Published: (2025)
Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
by: Azzaz, Riadh, et al.
Published: (2024)
by: Azzaz, Riadh, et al.
Published: (2024)
A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
by: Schoener, Brandon, et al.
Published: (2026)
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)
by: Mishra, Akshansh
Published: (2024)
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning
by: Xu, Changwen, et al.
Published: (2025)
by: Xu, Changwen, et al.
Published: (2025)
A Message Passing Neural Network Surrogate Model for Bond-Associated Peridynamic Material Correspondence Formulation
by: Hu, Xuan, et al.
Published: (2024)
by: Hu, Xuan, et al.
Published: (2024)
Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
by: Park, Eunjeong, et al.
Published: (2026)
by: Park, Eunjeong, et al.
Published: (2026)
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024)
by: Peng, Jiayu, et al.
Published: (2024)
Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials
by: Koker, Teddy, et al.
Published: (2023)
by: Koker, Teddy, et al.
Published: (2023)
Thermodynamically-Informed Iterative Neural Operators for Heterogeneous Elastic Localization
by: Kelly, Conlain, et al.
Published: (2024)
by: Kelly, Conlain, et al.
Published: (2024)
Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses
by: Jafarzadeh, Siavash, et al.
Published: (2024)
by: Jafarzadeh, Siavash, et al.
Published: (2024)
Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
by: Garban, Manuel Ricardo Guevara, et al.
Published: (2025)
by: Garban, Manuel Ricardo Guevara, et al.
Published: (2025)
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)
by: Li, Chenyang, et al.
Published: (2026)
Space Group Informed Transformer for Crystalline Materials Generation
by: Cao, Zhendong, et al.
Published: (2024)
by: Cao, Zhendong, et al.
Published: (2024)
Generative Models for Crystalline Materials
by: Metni, Houssam, et al.
Published: (2025)
by: Metni, Houssam, et al.
Published: (2025)
Generative Inversion for Property-Targeted Materials Design: Application to Shape Memory Alloys
by: Li, Cheng, et al.
Published: (2025)
by: Li, Cheng, et al.
Published: (2025)
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
by: Li, Longze, et al.
Published: (2023)
by: Li, Longze, et al.
Published: (2023)
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
by: Hardcastle, Christofer, et al.
Published: (2025)
by: Hardcastle, Christofer, 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)
by: Xing, Tengfei, et al.
Published: (2025)
MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis
by: Weng, Yonghao, et al.
Published: (2025)
by: Weng, Yonghao, et al.
Published: (2025)
Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials
by: Hollenweger, Yannick, et al.
Published: (2025)
by: Hollenweger, Yannick, et al.
Published: (2025)
Learning Physics-Consistent Material Behavior from Dynamic Displacements
by: Han, Zhichao, et al.
Published: (2024)
by: Han, Zhichao, et al.
Published: (2024)
Lagrangian Neural Networks for Reversible Dissipative Evolution
by: Sundararaghavan, Veera, et al.
Published: (2024)
by: Sundararaghavan, Veera, et al.
Published: (2024)
Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks
by: Nguyen, Tri Minh, et al.
Published: (2024)
by: Nguyen, Tri Minh, et al.
Published: (2024)
VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design
by: El-Awady, Khalid
Published: (2023)
by: El-Awady, Khalid
Published: (2023)
Orb: A Fast, Scalable Neural Network Potential
by: Neumann, Mark, et al.
Published: (2024)
by: Neumann, Mark, et al.
Published: (2024)
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
by: Zimmermann, Yoel, et al.
Published: (2024)
by: Zimmermann, Yoel, et al.
Published: (2024)
Similar Items
-
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
by: Song, Siyuan, et al.
Published: (2023) -
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
by: Li, Chaojian, et al.
Published: (2025) -
Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred Potential
by: Mao, Zetian, 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) -
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)