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tensorflow-physics-ml

TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models

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技能内容

TensorFlow Physics ML

Purpose

Provides expert guidance on TensorFlow for physics applications, including physics-informed neural networks and neural network potentials.

Capabilities

  • Physics-informed neural networks (PINNs)
  • Neural network potentials (NNP)
  • Normalizing flows for density estimation
  • Graph neural networks for molecular systems
  • Automatic differentiation for physics
  • TensorBoard experiment tracking

Usage Guidelines

  1. Architecture Design: Build appropriate neural network architectures
  2. PINNs: Incorporate physical constraints in loss functions
  3. Potentials: Train neural network interatomic potentials
  4. GNNs: Use graph networks for molecular systems
  5. Training: Monitor and optimize training with TensorBoard

Tools/Libraries

  • TensorFlow
  • DeepMD-kit
  • SchNet

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它属于哪个仓库

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本站分层T1
该仓技能数2115
原文件路径library/specializations/domains/science/physics/skills/tensorflow-physics-ml/SKILL.md

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