A Coupled Thermal-Mechanical Deep Material Network

A Data-Driven Approach to Print Performance Prediction

A Study Into The Generation and Usability of Synthetic Thermodynamic Data for Optimising the Manufacturing of Dual Phase Steels

A Study on Digital Tools for the Safe and Sustainable Design of Materials

A Thermodynamically Consistent Neural Ordinary Differential Equation for Constitutive Modeling of Polycrystalline Metals

Active Learning for Rapid Targeted Manufacturing of Thermoelectric Thin Film Alloys

Adapting to Uncontrolled Variables in Additive Manufacturing Systems

Advancing Data-Driven Uncertainties and Predictions of the Stress Strain Response of Polycrystalline Alloys

Advancing Materials Discovery and Manufacturing Optimization Through Large Language Models

AI-Ready Manufacturing Data for Cybersecurity

AI-Simulation Workflow to Accelerate Computational Discovery of Graphitization Product of Detonation Nanodiamonds

AI Guided Discovery of Lunar Derived Materials for a Sustainable Ecosystem

An Optimized Neural Network for Detecting Complex Glass Defects in Automated Inspection

Analysis of an Intelligent Characteristics of the Blast Furnace Peripheral Zone

ANN-Based Prediction of Steel Hardenability

Application of Deep Learning Approaches to Model the Heat Treatment Process-Microstructure-Property Relationship

Application of LLMs in Understanding Advanced Materials Properties and Manufacturing Processes

Application of ML to Ceramics Industry

Automated 3D Segmentation of Refractory Material Microstructures Using Deep Learning for Improved Corrosion Resistance

Bayesian Calibration and Uncertainty Quantification of a Cohesive Zone Model for Metal-Oxide Interfaces

Bayesian Data Assimilation in Latent Space for Phase-Field Simulation Using Variational Autoencoder

Beyond Bespoke Models: Foundational Vision Transformers for Microstructure Representation and Machine Learning of Microstructure-Property Relationships in Alloys

Causality and Saliency in Unimodal and Multimodal Datasets

Chemical and Materials Informatics for Rapid Toxin-Free Product Development

Computer Aided Design and Mesh Modeling Automation for FEA and Developing Surrogate Model

Conditional Continuous Normalizing Flows for Damage Studies in Unidirectional Fiber-Reinforced Composites

Correlation of ULTEM 9085 Physical, Chemical, and Mechanical Properties

Data-Driven Design of Architectured Materials: Enhancing Specific Stiffness With Multi-Objective Optimization

Data Driven and High Fidelity Modeling Approaches to Advance Understanding and TRL Level of 3D Printing

Deep Learning-Based Analysis of XRD Patterns for SOEC Cell Performance Evaluation

Deep Learning for Industrial-Scale Modeling of the Basic Oxygen Furnace Process

Detection of Keyhole-Pore Formation in Laser Powder Bed Fusion Using Spatio-Temporal Graph Convolutional Networks

Development of a Massively Parallel Reduced-Order Model Based Design/Optimization Tool for Power Generation Using Natural Gas-H2 Blended Fuels

Development of a Microstructure Image Generation Technique and Machine Learning Model for Property Prediction of As-Cast Materials

Development of Milling State Assessment System Using Machined Surface Images and Engineer's Sensory Evaluations as Supervised Data

Enhanced Resolution and Image Contrast in 3D XRM Data Using Deep Learning Based Reconstruction Methods

Enhancing Data Acquisition in Manufacturing: Leveraging LLMs for Effective Material Property Databases

Estimation of SOC in Electric Vehicle Batteries Using Machine Learning Models

Faster Prediction With AI/ML for Manufacturing: Using Simulations and Production Data Develop Fast-Running Inference Models and Knowledge Tools for Manufacturing

Fatigue Life and Fatigue Crack Growth Rate Prediction of Additively Manufactured Al 2024 Alloy Using Generative AI and Machine Learning Models

Generative Priors for Regularizing Ill-Posed Problems: Applications to 3D Polycrystalline RVE's

Harnessing AI for Precision Manufacturing of Nanofibers Membranes

Harnessing Large Language Models for Information Extraction and Material Design

Hierarchical Bayesian Modeling for Enhanced Contamination Detection in Electron Beam Powder Bed Fusion Processes

High-Fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations

Innovative AI Method for Predicting Grain Size Distribution During Grain Growth Mechanism

Leveraging CALPHAD to Overcome Data Challenges in Machine Learning for Materials Science

Leveraging Generative Models to Optimize Steel Alloys From Recycled Materials

LLM-Assisted Data Curation in Starrydata: An Open Database of Material Properties Extracted From Published Plots

Long-Term Durability of GFRP Rebars in the Alkaline Environment Under Sustained Loading Using Machine Learning and Empirical Modelling

Machine Learning-Based Prediction of Diffusion Coefficients in Alloys

Machine Learning-Based Prediction of Temperature Fields in Synchronized Multi-Laser Powder Bed Additive Manufacturing (S-MLAM)

Machine Learning-Driven Discovery of High-Hardness Multi-Principal Element Alloys With Physics Informed Priors

Machine Learning Applications in Manufacturing Operations Environment

Machine Learning of Mode Filter Grain Growth Model Characteristics

MatCHMaker: Machine Learning for Microstructural Analysis in Materials Characterization Modeling

Metallographic Image and Mask Generation Based on Denoising Deffusion Probabilistic Models for Improving Metallographic Image Segmentation

MLOgraphy++: A Context-Enhanced U-Net Approach for Robust Grain Boundary Segmentation in Metallographic Images

Modern Methods for Understanding the Electrochemical Stability of Inorganic Materials

Multidimensional Analysis for Correlation of Mechano-Physico-Chemical Attributes With Bio-Functionality in Eight TiNbZrSnTa (TNZST) Alloys

Offline Programming for Wire-Arc DED Applications Using Machine Learning Algorithms

On the Use of Decoder-Only Transformers to Model Time-Series based Silicon Data

Ontology-Based Digital Representations of Materials Testing in the MaterialDigital Initiative

Optimal Design Method Using High-Fidelity Surrogate Modeling Based on Finite Element analysis Data

Optimization of Machining Conditions for Improved Machining Quality Based on a Digital Twin of Machine Tools

Out-of-Distribution Surface Anomaly Detection Using Masked Autoencoder Vision Transformers

Performance Analysis of Different Shaped Tool Electrodes During Electrical Discharge Machining of Inconel 718

Physical-Informed Machine Learning for Silicone Formulation Development

Physically Aligned Hierarchical Mesh-Based Network for Dynamic System Simulation

Physics-Informed Generative AI for Predicting Material Deformation: Latent Diffusion Modeling From Undeformed Microstructures

Physics-Informed ML for Crystal Growth Manufacturing

Prediction of Creep Life Using K-Means Clustering and Gaussian Process Regression

Process Control System and Cloud Simulation Platform for Continuous Casting Based on Digital Twin Technology

Quantum Machine Learning: Revolutionizing Fatigue Fracture Mode Prediction of Laser Powder Processed Inconel 625

Rapid Bubble and Cavity Tracking Utilizing Machine Learning

ROM-Net in Additive Manufacturing

Salvaging of Materials Fatigue Data from Literature Using Language Model Systems

Simulation-Based Optimization of Additive Manufacturing Toolpaths to Reduce Distortion

Strength in Uncertainty: Federated Learning for Accelerated Innovation

Strongly Physics-Constrained Neural Networks for Mechanical Superresolution

Surrogate Modeling of Cluster Dynamics-Predicted Nucleation and Growth of Irradiation Defects Using Time-Series Neural Networks

Synthetic Microstructure Generation and Prediction Using Stable Diffusion

Texture Evolution Surrogate for Magnesium Materials

The HPC4EI Program Connects Industry Partners With National Lab HPC and AI/ML Resources

The Variational Deep Materials Network: Efficient Extrapolation With Uncertainty of Homogenized Material Responses

Topological Analysis of Processing to Microstructure Mappings

Transfer Learning for Nanomaterial Classification

Transfer Learning for Ultrasonic Crystallography: Accelerating Orientation Mapping in Materials With Neural Networks

Understanding Manufacturing and Materials Design Spaces: Overcoming Expensive Data

Unsupervised Learning for Low Dimensional Corrosion Quantification of Aluminum Films

Utilizing Large Language Models for Interpreting & Interfacing With Instance Segmentation Results of Metallic Powder Micrographs

Utilizing Time-Series Data for Improved Prediction of End-Point Temperature and Carbon in Basic Oxygen Furnace With a Large Industrial Dataset

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