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Physics NeMo - NVIDIA 开源物理机器学习框架
NVIDIA Physics NeMo is an open-source deep-learning framework designed for building, training, and fine-tuning deep learning models utilizing state-of-the-art Physics-informed Machine Learning (Physics-ML) methods. Accelerate scientific computing and engineering simulations.
Python
Added on 2025年6月12日1,597
Stars362
ForksPython
LanguageProject Introduction
Summary
Physics NeMo is NVIDIA's open-source framework empowering researchers and engineers to build and deploy deep learning models that respect the underlying physics of the system they model. It provides the building blocks and tools necessary for state-of-the-art Physics-ML development.
Problem Solved
Traditional data-driven deep learning models often lack physical consistency, struggle with extrapolation beyond training data, and require vast amounts of labeled data. Physics-ML addresses these issues by embedding physical principles, leading to more robust, interpretable, and data-efficient models, particularly crucial in scientific and engineering domains.
Core Features
Physics-Informed Neural Networks (PINNs)
Includes a library of pre-built neural network modules and architectures specifically designed for integrating physical constraints and laws.
Accelerated Training & Inference
Provides tools and workflows optimized for training models that combine data-driven approaches with physical equations on accelerated hardware.
Seamless Data Integration
Supports integration with existing scientific simulation data and environments to enhance model accuracy and generalizability.
Tech Stack
PyTorch
CUDA
Distributed Training Libraries
Physics Simulation Interfaces
使用场景
Physics NeMo is applicable across various scientific and engineering disciplines where deep learning can benefit from incorporating physical laws.
场景一：求解偏微分方程 (PDEs)
Details
Develop PINNs to solve partial differential equations (PDEs) that govern fluid flow, heat transfer, or structural mechanics without needing large datasets from traditional solvers.
User Value
Enable faster simulation and analysis of complex physical systems; discover new solutions space.
场景二：材料科学与化学反应预测
Details
Build models that predict material properties or reaction dynamics by embedding known physical relationships, improving accuracy and generalizability beyond training data.
User Value
Accelerate materials discovery and process optimization; develop more robust predictive models.
场景三：气候与天气建模
Details
Create data-driven models for climate or weather forecasting that respect atmospheric physics, leading to more stable and accurate long-term predictions.
User Value
Improve the reliability and interpretability of climate models; enhance forecasting accuracy.
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