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Meeting MS&T23: Materials Science & Technology
Symposium Leveraging Integrated Computational Materials Engineering for High-fidelity Physics-based and Machine Learning Models
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OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Application of Machine Learning Framework in Predicting Creep Response of High Temperature Alloys
Hybrid Simulation Method Based on Molecular Dynamics and Machine Learning to Improve Property Prediction with Lower Computational Cost in Complex System
New Refractory High Entropy Alloys Discovery by Physics Discovery
Novel Convolutional-Recurrent Hybrid Neural Network for Predicting Fission Gas Release in UO2 Nuclear Fuel
Robotic Bending of Craniomaxillofacial Graft Fixation Plates
Simulating Macroscale Microstructures Using Advanced Programming and Numerical Methods

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