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Meeting MS&T24: Materials Science & Technology
Symposium Machine Learning and Simulations
Presentation Title Forecasting Nutrient Flows Using Terrain Elevation-Aware Spatial-Temporal Graph Neural Networks
Author(s) Jiaqi Li, Alexander H. Bradley, Olatunde Akanbi, Laura Bruckman, Erika Barcelos, Roger French, Yinghui Wu
On-Site Speaker (Planned) Jiaqi Li
Abstract Scope We propose a class of Terrain Elevation-aware Spatio-Temporal Graph Neural Networks (ST-EGNN) that incorporates terrain elevation data to its edge model to improve the accuracy of time-series forecasting based agricultural nutrient flow analysis. Results were compared against three baseline models: (a) a spatial-temporal Graph Neural Network that only use edges induced from 2D Euclidean distances, and two deep neural networks: (b) sequence to sequence (seq2seq), which is a class of deep learning architectures renowned for their accomplishments in various domain, and (c) LSTM is a type of recurrent neural network architecture designed to overcome the vanishing gradient problem in traditional RNNs, respectively. In addition, the framework leverages data imputation from multiple sensors concurrently to improve the robustness of STEGNN against the impact of missing data. By learning spatial, temporal and elevation patterns, our approach endeavors to offer a more adaptable predictive framework for nutrition analysis in water and river systems.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Machine Learning Approach to Predict Solute Segregation Energy in Ni Grain Boundaries
A Machine Learning Based Computational Method for Accurate Prediction of Equilibrium Cation Distribution in Complex Spinel Oxides
Assessing GPR Models for Steel Hardness Prediction in Production Environments
Decoding the Structural Genome of Silicate Glasses
EBSD Geometry Calibration Through SE(3) Lie Group Optimization
End-To-End Differentiability and Tensor Processing Unit (TPU) Computing to Accelerate Materials’ Inverse Design
Estimation of Thermal Hysteresis in Zirconia Using Machine Learning Molecular Dynamics and Transition State Modelling
Forecasting Nutrient Flows Using Terrain Elevation-Aware Spatial-Temporal Graph Neural Networks
Forward Prediction and Inverse Design of Additively Manufacturable Alloys via Autoregressive Language Models
Generation of Machine Learning Interatomic Potentials for Boron Carbide with Comparison to the Analytic Angular Dependent Potential
Graph Neural Networks for Rapid Continuum Damage Modeling of Semi-Crystalline Polymers
Machine Learning in Nuclear Waste Glass Formulation and Property Model Development
Multi-Fidelity Gaussian Process Models for Time-Series Outputs
New Machine–Learning Interatomic Potentials (MLIPs) for Si-C-O-H Compounds Enabling Atomistic Simulations of Complex Chemical Transformations
On Languaging a Simulation Engine
Predicting the Dynamics of Atoms in Liquids by a Surrogate Machine-Learned Simulator
Understanding Grain-Boundary Structure Using Strain Functional Descriptors and Unsupervised Machine Learning
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