tensorflow-physics-ml

Included with Lifetime

$97 forever

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

General

What this skill does


# 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

Files: 1

Size: 1.1 KB

Complexity: 9/100

Category: General

Source: https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/physics/skills/tensorflow-physics-ml

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