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Computer Science and Mathematics

Events

  • 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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