TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
--- name: tensorflow-physics-ml-2a18b6a69ce2 description: TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: physics domain: science category: data-analysis phase: 6 graph: domains: [domain:physics] skillAreas: [skill-area:machine-learning-frameworks, skill-area:physics-simulation, skill-area:statistical-analysis] workflows: [workflow:experiment-design, workflow:peer-review-cycle] roles: [role:research-scientist, role:ml-engineer] --- # 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--- name: tensorflow-physics-ml-2a18b6a69ce2 description: TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: physics domain: science category: data-analysis phase: 6 graph: domains: [domain:physics] skillAreas: [skill-area:machine-learning-frameworks, skill-area:physics-simulation, skill-area:statistical-analysis] workflows: [workflow:experiment-design, workflow:peer-review-cycle] roles: [role:research-scientist, role:ml-engineer] --- # 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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.