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Data-driven Surrogate Modeling of Turbulent Flows in the Atmosphere and Ocean
Multi-species BGK models
TaylorNet: A Taylor-Driven Generic Neural Architecture
Data-Driven and Knowledge-Driven Deep Learning for Battery Safety Modeling
Towards Third Wave AI: Interpretable, Robust Trustworthy Machine Learning for Diverse Applications in Science and Engineering
Data-driven Surrogate Modeling and Sensitivity Analysis for Particle-In-Cell Simulations of Plasma-Material Interactions
Interpreting Neural Network Models through Graph Representation Learning
Estimating the Errors of Randomized Algorithms via the Bootstrap
Stable Algorithms for Computing Forward Sensitivities for Particle-in-Cell Methods
Mathematica Tutorial
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